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0 | easy_stock-future price | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 9 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 9 hours. Your goal is to make the most accurate prediction. The historical stock value data of PX for the past 15 days is: [10.01, 10.06, 10.0, 10.0, 9.97, 10.09, 10.02, 10.05, 10.04, 10.02, 10.03, 10.02, 10.02, 9.98, 9.95]. The historical stock value data of KIND for the past 15 days is: [2.27, 2.28, 2.28, 2.28, 2.27, 2.3, 2.28, 2.29, 2.3, 2.28, 2.28, 2.28, 2.26, 2.27, 2.23]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_0.pkl | external_data/ground_truth_data/ground_truth_data_0.pkl | external_data/context/context_0.pkl | external_data/constraint/constraint_0.pkl |
1 | easy_stock-future price | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 19 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 19 hours. Your goal is to make the most accurate prediction. The historical stock value data of NCV for the past 10 days is: [3.38, 3.37, 3.37, 3.38, 3.38, 3.38, 3.38, 3.37, 3.36, 3.37]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_1.pkl | external_data/ground_truth_data/ground_truth_data_1.pkl | external_data/context/context_1.pkl | external_data/constraint/constraint_1.pkl |
2 | easy_stock-future price | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. The historical stock value data of SKF for the past 13 days is: [10.53, 10.57, 10.56, 10.47, 10.49, 10.51, 10.59, 10.73, 10.81, 10.76, 10.84, 10.8, 10.83]. The historical stock value data of XOM for the past 13 days is: [114.29, 113.98, 113.61, 113.51, 113.38, 113.18, 113.23, 112.49, 112.39, 112.38, 112.0, 112.17, 112.68]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_2.pkl | external_data/ground_truth_data/ground_truth_data_2.pkl | external_data/context/context_2.pkl | external_data/constraint/constraint_2.pkl |
3 | easy_stock-future price | I have the past 76 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 27 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 76 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 27 days. Your goal is to make the most accurate prediction. The historical stock value data of PCQ for the past 76 days is: [9.06, 8.94, 8.95, 8.98, 8.97, 8.88, 8.87, 8.92, 8.91, 8.92, 9.04, 9.1, 9.14, 9.21, 9.26, 9.3, 9.2, 9.29, 9.31, 9.29, 9.28, 9.27, 9.25, 9.15, 9.11, 9.08, 9.1, 9.05, 8.93, 8.93, 8.93, 8.98, 9.03, 9.09, 9.15, 9.14, 9.22, 9.21, 9.25, 9.27, 9.31, 9.26, 9.28, 9.27, 9.25, 9.24, 9.23, 9.25, 9.34, 9.31, 9.22, 9.25, 9.28, 9.32, 9.3, 9.27, 9.29, 9.38, 9.45, 9.41, 9.45, 9.38, 9.34, 9.34, 9.31, 9.35, 9.29, 9.31, 9.32, 9.33, 9.29, 9.34, 9.42, 9.47, 9.37, 9.46]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_3.pkl | external_data/ground_truth_data/ground_truth_data_3.pkl | external_data/context/context_3.pkl | external_data/constraint/constraint_3.pkl |
4 | easy_stock-future price | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 14 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 14 hours. Your goal is to make the most accurate prediction. The historical stock value data of NAK for the past 12 days is: [0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.32, 0.33, 0.33, 0.33]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_4.pkl | external_data/ground_truth_data/ground_truth_data_4.pkl | external_data/context/context_4.pkl | external_data/constraint/constraint_4.pkl |
5 | easy_stock-future price | I have the past 16 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 17 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 16 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 17 hours. Your goal is to make the most accurate prediction. The historical stock value data of ZYXI for the past 16 days is: [7.82, 7.89, 7.86, 7.83, 7.81, 7.88, 7.74, 7.78, 7.75, 7.8, 7.78, 7.78, 7.84, 7.82, 7.8, 7.8]. The historical stock value data of WDFC for the past 16 days is: [264.46, 264.81, 264.43, 264.62, 264.11, 263.13, 265.66, 262.04, 263.11, 263.27, 262.19, 263.58, 261.41, 259.35, 258.0, 259.56]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_5.pkl | external_data/ground_truth_data/ground_truth_data_5.pkl | external_data/context/context_5.pkl | external_data/constraint/constraint_5.pkl |
6 | easy_stock-future price | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 5 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 5 hours. Your goal is to make the most accurate prediction. The historical stock value data of MHO for the past 15 days is: [156.3, 156.33, 158.2, 156.77, 157.01, 158.82, 157.82, 157.34, 157.21, 157.27, 158.35, 158.74, 158.45, 157.26, 157.14]. The historical stock value data of TD for the past 15 days is: [60.57, 60.53, 60.73, 60.16, 59.86, 60.22, 60.25, 60.44, 60.38, 61.29, 61.25, 61.15, 61.36, 61.42, 61.45]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_6.pkl | external_data/ground_truth_data/ground_truth_data_6.pkl | external_data/context/context_6.pkl | external_data/constraint/constraint_6.pkl |
7 | easy_stock-future price | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. The historical stock value data of SLQT for the past 12 days is: [3.47, 3.43, 3.38, 3.44, 3.4, 3.45, 3.36, 3.31, 3.29, 3.32, 3.27, 3.32]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_7.pkl | external_data/ground_truth_data/ground_truth_data_7.pkl | external_data/context/context_7.pkl | external_data/constraint/constraint_7.pkl |
8 | easy_stock-future price | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 19 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 19 hours. Your goal is to make the most accurate prediction. The historical stock value data of NBTB for the past 11 days is: [47.05, 47.13, 47.04, 47.14, 46.57, 46.44, 46.48, 46.66, 46.35, 46.67, 46.27]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_8.pkl | external_data/ground_truth_data/ground_truth_data_8.pkl | external_data/context/context_8.pkl | external_data/constraint/constraint_8.pkl |
9 | easy_stock-future price | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 23 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 23 days. Your goal is to make the most accurate prediction. The historical stock value data of ONCT for the past 37 days is: [9.3, 10.03, 10.15, 9.5, 9.55, 9.25, 9.59, 9.8, 9.0, 9.05, 7.82, 8.02, 7.8, 7.88, 8.35, 7.96, 8.22, 8.21, 8.05, 8.2, 9.0, 9.3, 9.27, 9.0, 8.8, 8.65, 9.0, 8.62, 8.7, 8.94, 9.4, 8.8, 8.69, 8.95, 8.6, 8.86, 8.55]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_9.pkl | external_data/ground_truth_data/ground_truth_data_9.pkl | external_data/context/context_9.pkl | external_data/constraint/constraint_9.pkl |
10 | easy_stock-future price | I have the past 73 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 73 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. The historical stock value data of TDF for the past 73 days is: [8.72, 8.67, 8.65, 8.47, 8.51, 8.46, 8.3, 8.17, 8.27, 8.17, 8.05, 8.05, 8.04, 8.04, 8.11, 8.13, 8.19, 8.36, 8.05, 7.96, 7.85, 7.9, 7.93, 8.04, 8.05, 7.91, 7.87, 7.78, 7.76, 7.68, 7.54, 7.52, 7.59, 7.55, 7.4, 7.24, 7.29, 7.33, 7.16, 7.4, 7.59, 7.53, 7.48, 7.37, 7.2, 7.2, 7.25, 7.13, 7.21, 7.59, 7.51, 7.41, 7.45, 7.59, 7.47, 7.57, 7.55, 7.65, 7.58, 7.71, 7.78, 7.79, 7.74, 7.79, 7.62, 7.64, 7.75, 7.66, 7.64, 7.73, 7.64, 7.64, 7.79]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_10.pkl | external_data/ground_truth_data/ground_truth_data_10.pkl | external_data/context/context_10.pkl | external_data/constraint/constraint_10.pkl |
11 | easy_stock-future price | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 16 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 16 hours. Your goal is to make the most accurate prediction. The historical stock value data of SMCX for the past 18 days is: [8.15, 8.1, 8.29, 8.01, 8.06, 8.02, 8.12, 7.32, 7.13, 7.14, 7.16, 7.1, 7.2, 7.02, 7.36, 7.41, 7.85, 8.0]. The historical stock value data of QMCO for the past 18 days is: [2.68, 2.67, 2.62, 2.65, 2.62, 2.58, 2.52, 2.33, 2.39, 2.31, 2.33, 2.32, 2.54, 2.51, 2.69, 2.69, 2.75, 2.76]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_11.pkl | external_data/ground_truth_data/ground_truth_data_11.pkl | external_data/context/context_11.pkl | external_data/constraint/constraint_11.pkl |
12 | easy_stock-future price | I have the past 85 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 18 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 85 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 18 days. Your goal is to make the most accurate prediction. The historical stock value data of MED for the past 85 days is: [52.04, 53.28, 54.47, 54.02, 53.42, 54.88, 55.23, 55.81, 56.87, 54.65, 54.76, 53.04, 51.67, 53.87, 54.05, 54.68, 53.53, 56.13, 52.88, 52.68, 51.46, 49.99, 48.93, 40.38, 40.0, 40.73, 40.56, 41.75, 40.57, 40.09, 40.44, 39.5, 38.83, 38.04, 38.09, 39.66, 37.66, 36.6, 35.75, 34.57, 36.61, 35.85, 34.94, 35.74, 35.64, 34.7, 35.8, 37.07, 37.53, 38.32, 37.51, 35.12, 34.06, 33.88, 32.83, 33.14, 34.05, 31.89, 31.94, 31.51, 31.35, 32.25, 31.88, 32.47, 32.44, 32.6, 33.42, 33.57, 33.5, 33.71, 35.51, 27.53, 26.36, 25.93, 26.69, 25.18, 25.51, 24.96, 25.72, 24.99, 25.35, 26.44, 25.36, 25.36, 25.0]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_12.pkl | external_data/ground_truth_data/ground_truth_data_12.pkl | external_data/context/context_12.pkl | external_data/constraint/constraint_12.pkl |
13 | easy_stock-future price | I have the past 43 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 27 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 43 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 27 days. Your goal is to make the most accurate prediction. The historical stock value data of QDF for the past 43 days is: [62.0, 61.8, 62.39, 62.9, 63.0, 62.62, 62.86, 63.11, 62.19, 61.91, 62.33, 63.34, 63.82, 63.94, 63.9, 64.28, 64.36, 64.57, 64.83, 65.62, 65.49, 65.38, 65.47, 65.42, 65.13, 64.63, 64.84, 64.66, 64.15, 64.16, 64.84, 64.87, 64.94, 65.49, 65.39, 65.35, 65.49, 65.66, 66.22, 66.42, 66.33, 66.83, 67.11]. The historical stock value data of SIG for the past 43 days is: [93.37, 94.86, 99.47, 100.43, 97.96, 99.31, 101.44, 101.78, 97.69, 95.61, 97.06, 94.02, 96.84, 96.41, 94.28, 95.51, 96.57, 99.09, 100.83, 101.85, 101.09, 98.5, 97.95, 99.53, 98.69, 101.01, 102.38, 104.19, 103.96, 105.46, 109.11, 107.97, 106.98, 106.39, 106.35, 105.36, 103.4, 105.26, 108.04, 91.93, 86.87, 91.28, 90.92]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_13.pkl | external_data/ground_truth_data/ground_truth_data_13.pkl | external_data/context/context_13.pkl | external_data/constraint/constraint_13.pkl |
14 | easy_stock-future price | I have the past 13 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 7 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 13 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 7 hours. Your goal is to make the most accurate prediction. The historical stock value data of PCAR for the past 13 days is: [93.23, 94.51, 93.95, 94.32, 94.31, 94.2, 94.68, 94.97, 94.29, 94.53, 94.19, 94.4, 94.24]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_14.pkl | external_data/ground_truth_data/ground_truth_data_14.pkl | external_data/context/context_14.pkl | external_data/constraint/constraint_14.pkl |
15 | easy_stock-future price | I have the past 34 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 17 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 34 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 17 days. Your goal is to make the most accurate prediction. The historical stock value data of SIXG for the past 34 days is: [30.6, 30.75, 30.87, 30.4, 30.53, 30.99, 31.0, 31.06, 30.6, 30.56, 30.43, 30.9, 31.01, 31.22, 31.28, 31.18, 30.61, 31.09, 30.78, 30.29, 29.95, 29.52, 29.39, 29.67, 28.91, 28.59, 28.77, 28.78, 29.16, 29.49, 30.06, 30.6, 30.6, 30.85]. The historical stock value data of POCI for the past 34 days is: [6.1, 6.09, 5.45, 6.0, 6.0, 6.0, 5.95, 6.15, 6.15, 6.0, 6.15, 6.47, 6.26, 6.0, 5.98, 6.15, 6.08, 5.93, 5.75, 5.8, 6.18, 6.01, 5.75, 5.74, 5.61, 5.75, 5.62, 5.79, 5.79, 5.77, 5.99, 6.35, 6.35, 5.86]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_15.pkl | external_data/ground_truth_data/ground_truth_data_15.pkl | external_data/context/context_15.pkl | external_data/constraint/constraint_15.pkl |
16 | easy_stock-future price | I have the past 19 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 14 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 19 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 14 hours. Your goal is to make the most accurate prediction. The historical stock value data of VTR for the past 19 days is: [63.32, 62.78, 62.71, 62.61, 62.66, 62.68, 62.82, 62.38, 62.36, 62.39, 62.37, 62.54, 63.08, 62.86, 63.17, 63.42, 63.76, 64.04, 64.05]. The historical stock value data of TRDA for the past 19 days is: [15.13, 15.13, 15.32, 15.41, 15.44, 15.51, 15.38, 14.97, 14.85, 14.62, 14.98, 14.84, 14.88, 14.78, 15.14, 15.08, 15.2, 15.12, 15.03]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_16.pkl | external_data/ground_truth_data/ground_truth_data_16.pkl | external_data/context/context_16.pkl | external_data/constraint/constraint_16.pkl |
17 | easy_stock-future price | I have the past 47 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 28 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 47 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 28 days. Your goal is to make the most accurate prediction. The historical stock value data of NSPR for the past 47 days is: [2.62, 2.66, 2.81, 2.71, 2.61, 2.62, 2.63, 2.88, 2.85, 2.92, 2.88, 2.9, 3.06, 3.14, 3.01, 3.1, 3.11, 3.14, 3.05, 2.94, 2.94, 3.04, 2.95, 2.83, 2.86, 2.83, 2.75, 2.92, 2.85, 2.76, 2.75, 2.74, 2.72, 2.68, 2.69, 2.65, 2.61, 2.6, 2.69, 2.63, 2.56, 2.73, 2.77, 2.75, 2.66, 2.57, 2.63]. The historical stock value data of SOWG for the past 47 days is: [9.72, 10.05, 10.05, 10.05, 10.25, 8.75, 8.8, 8.8, 7.52, 8.2, 8.0, 8.0, 9.0, 8.25, 8.25, 7.48, 7.53, 8.0, 8.12, 8.12, 8.12, 7.2, 7.34, 7.13, 7.13, 7.4, 8.5, 8.35, 8.35, 8.35, 9.7, 7.55, 7.55, 7.5, 7.5, 7.97, 7.52, 7.52, 7.52, 7.26, 7.26, 7.62, 7.62, 9.25, 9.25, 8.5, 9.5]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_17.pkl | external_data/ground_truth_data/ground_truth_data_17.pkl | external_data/context/context_17.pkl | external_data/constraint/constraint_17.pkl |
18 | easy_stock-future price | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 22 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 22 hours. Your goal is to make the most accurate prediction. The historical stock value data of SRDX for the past 11 days is: [38.9, 38.79, 38.83, 38.81, 38.86, 38.83, 39.0, 38.82, 38.72, 38.82, 38.84]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_18.pkl | external_data/ground_truth_data/ground_truth_data_18.pkl | external_data/context/context_18.pkl | external_data/constraint/constraint_18.pkl |
19 | easy_stock-future price | I have the past 16 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 16 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 10 hours. Your goal is to make the most accurate prediction. The historical stock value data of LPLA for the past 16 days is: [211.07, 211.24, 210.7, 210.53, 211.54, 211.39, 212.68, 212.38, 211.99, 211.72, 210.65, 210.16, 209.96, 209.54, 210.63, 207.79]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_19.pkl | external_data/ground_truth_data/ground_truth_data_19.pkl | external_data/context/context_19.pkl | external_data/constraint/constraint_19.pkl |
20 | easy_stock-future price | I have the past 97 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 97 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. The historical stock value data of SMB for the past 97 days is: [16.55, 16.57, 16.55, 16.57, 16.57, 16.6, 16.6, 16.61, 16.6, 16.6, 16.63, 16.71, 16.7, 16.76, 16.7, 16.75, 16.78, 16.77, 16.78, 16.79, 16.78, 16.83, 16.84, 16.85, 16.83, 16.82, 16.83, 16.83, 16.81, 16.85, 16.87, 16.81, 16.83, 16.82, 16.81, 16.8, 16.82, 16.83, 16.81, 16.8, 16.8, 16.81, 16.81, 16.79, 16.76, 16.77, 16.78, 16.76, 16.76, 16.79, 16.78, 16.8, 16.8, 16.85, 16.86, 16.82, 16.77, 16.81, 16.81, 16.78, 16.79, 16.82, 16.78, 16.79, 16.8, 16.79, 16.81, 16.81, 16.81, 16.83, 16.82, 16.82, 16.83, 16.84, 16.87, 16.83, 16.85, 16.85, 16.87, 16.86, 16.85, 16.86, 16.86, 16.83, 16.84, 16.84, 16.87, 16.85, 16.84, 16.84, 16.82, 16.81, 16.82, 16.82, 16.81, 16.78, 16.79]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_20.pkl | external_data/ground_truth_data/ground_truth_data_20.pkl | external_data/context/context_20.pkl | external_data/constraint/constraint_20.pkl |
21 | easy_stock-future price | I have the past 39 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 24 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 39 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the stock price for the future 24 days. Your goal is to make the most accurate prediction. The historical stock value data of TRVI for the past 39 days is: [1.11, 1.11, 1.07, 1.15, 1.09, 1.15, 1.15, 1.25, 1.27, 1.28, 1.28, 1.29, 1.3, 1.29, 1.36, 1.39, 1.43, 1.44, 1.39, 1.34, 1.3, 1.31, 1.38, 1.32, 1.36, 1.35, 1.33, 1.46, 1.42, 1.36, 1.35, 1.36, 1.35, 1.31, 1.35, 1.34, 1.33, 1.56, 1.59]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_21.pkl | external_data/ground_truth_data/ground_truth_data_21.pkl | external_data/context/context_21.pkl | external_data/constraint/constraint_21.pkl |
22 | easy_stock-future price | I have the past 60 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. Please give me your prediction, return it as a 2d numpy array.The historical stock value data is stored in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 60 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the stock price for the future 12 days. Your goal is to make the most accurate prediction. The historical stock value data of VIRC for the past 60 days is: [7.21, 7.9, 6.33, 6.62, 6.69, 6.94, 7.25, 7.82, 7.31, 7.37, 7.34, 6.58, 7.77, 8.01, 7.4, 7.27, 6.93, 6.37, 6.56, 6.73, 6.44, 6.01, 6.06, 6.06, 6.26, 6.25, 6.03, 6.11, 6.42, 6.43, 6.68, 7.05, 6.11, 6.35, 6.55, 6.3, 6.28, 6.6, 6.75, 6.32, 6.47, 6.45, 6.36, 6.37, 6.68, 6.91, 7.29, 6.84, 6.79, 6.97, 8.47, 8.99, 9.08, 9.48, 9.8, 10.45, 10.79, 11.13, 11.08, 10.86]. The historical stock value data of ZTEK for the past 60 days is: [1.24, 1.25, 1.22, 1.18, 1.24, 1.23, 1.2, 1.23, 1.2, 1.15, 1.14, 1.2, 1.19, 1.15, 1.16, 1.19, 1.24, 1.22, 1.26, 1.25, 1.24, 1.27, 1.29, 1.35, 1.3, 1.26, 1.26, 1.27, 1.26, 1.24, 1.21, 1.21, 1.2, 1.13, 1.14, 1.1, 1.11, 1.18, 1.15, 1.12, 1.1, 1.09, 1.04, 1.16, 1.25, 1.26, 1.19, 1.2, 1.18, 1.07, 1.1, 1.09, 1.08, 1.08, 1.07, 1.07, 1.06, 1.14, 1.07, 1.06]. Please give me your prediction, return it as a 2d numpy array. | external_data/executor_variables/executor_variables_22.pkl | external_data/ground_truth_data/ground_truth_data_22.pkl | external_data/context/context_22.pkl | external_data/constraint/constraint_22.pkl |
23 | easy_stock-future volatility | I have the past 57 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 57 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 days. Your goal is to make the most accurate prediction .The historical stock value data of SHLS for the past 57 days is: [8.45, 8.5, 8.73, 9.0, 8.85, 8.8, 7.51, 7.68, 6.94, 7.13, 7.03, 6.79, 6.84, 6.41, 6.6, 6.8, 7.64, 7.62, 8.05, 7.95, 8.29, 8.17, 7.87, 7.16, 7.19, 7.22, 7.02, 6.81, 6.81, 7.45, 7.07, 6.79, 6.48, 6.66, 6.88, 6.58, 6.51, 6.49, 6.29, 6.28, 6.48, 6.24, 6.11, 6.0, 6.03, 5.95, 6.08, 6.24, 6.3, 6.41, 6.87, 6.8, 7.06, 6.81, 6.63, 6.59, 6.64]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_23.pkl | external_data/ground_truth_data/ground_truth_data_23.pkl | external_data/context/context_23.pkl | external_data/constraint/constraint_23.pkl |
24 | easy_stock-future volatility | I have the past 30 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 18 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 30 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 18 days. Your goal is to make the most accurate prediction .The historical stock value data of OEUR for the past 30 days is: [30.54, 30.07, 30.3, 30.45, 30.5, 30.33, 30.55, 30.59, 30.42, 30.33, 30.28, 30.36, 30.33, 30.5, 30.74, 30.59, 30.33, 30.76, 30.87, 31.31, 30.94, 30.99, 30.76, 30.4, 30.27, 30.72, 30.52, 30.09, 30.04, 30.37]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_24.pkl | external_data/ground_truth_data/ground_truth_data_24.pkl | external_data/context/context_24.pkl | external_data/constraint/constraint_24.pkl |
25 | easy_stock-future volatility | I have the past 18 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 13 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 18 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 13 hours. Your goal is to make the most accurate prediction .The historical stock value data of VKI for the past 18 days is: [9.06, 9.09, 9.09, 9.09, 9.09, 9.12, 9.12, 9.13, 9.14, 9.14, 9.18, 9.19, 9.19, 9.19, 9.18, 9.19, 9.21, 9.2]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_25.pkl | external_data/ground_truth_data/ground_truth_data_25.pkl | external_data/context/context_25.pkl | external_data/constraint/constraint_25.pkl |
26 | easy_stock-future volatility | I have the past 10 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 20 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 10 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 20 hours. Your goal is to make the most accurate prediction .The historical stock value data of LZ for the past 10 days is: [6.55, 6.54, 6.59, 6.53, 6.54, 6.54, 6.45, 6.43, 6.45, 6.41]. The historical stock value data of RSHO for the past 10 days is: [34.62, 34.71, 34.8, 34.86, 34.84, 34.86, 34.46, 34.41, 34.41, 34.4]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_26.pkl | external_data/ground_truth_data/ground_truth_data_26.pkl | external_data/context/context_26.pkl | external_data/constraint/constraint_26.pkl |
27 | easy_stock-future volatility | I have the past 16 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 7 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 16 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 7 hours. Your goal is to make the most accurate prediction .The historical stock value data of PFS for the past 16 days is: [18.66, 18.45, 18.23, 18.27, 18.37, 18.28, 18.33, 18.29, 18.2, 17.99, 18.08, 18.02, 18.01, 17.97, 18.0, 17.75]. The historical stock value data of MOH for the past 16 days is: [331.29, 330.72, 331.42, 331.49, 331.79, 329.94, 327.99, 327.47, 329.9, 327.39, 327.86, 326.71, 325.65, 325.17, 323.75, 332.39]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_27.pkl | external_data/ground_truth_data/ground_truth_data_27.pkl | external_data/context/context_27.pkl | external_data/constraint/constraint_27.pkl |
28 | easy_stock-future volatility | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 13 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 13 hours. Your goal is to make the most accurate prediction .The historical stock value data of SPSM for the past 19 days is: [43.75, 43.7, 43.83, 43.76, 43.72, 43.24, 43.01, 42.94, 43.14, 42.98, 43.07, 42.97, 42.95, 42.92, 43.1, 43.11, 42.97, 42.97, 42.86]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_28.pkl | external_data/ground_truth_data/ground_truth_data_28.pkl | external_data/context/context_28.pkl | external_data/constraint/constraint_28.pkl |
29 | easy_stock-future volatility | I have the past 45 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 12 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 45 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 12 days. Your goal is to make the most accurate prediction .The historical stock value data of RM for the past 45 days is: [23.73, 23.95, 21.44, 22.44, 21.09, 21.08, 20.94, 21.4, 21.78, 22.43, 23.7, 22.4, 21.23, 21.96, 22.08, 21.4, 21.05, 21.47, 20.96, 20.44, 20.89, 21.4, 22.49, 22.55, 21.69, 21.67, 22.28, 22.62, 22.52, 22.53, 23.54, 24.51, 24.27, 23.88, 24.88, 24.83, 25.71, 26.23, 26.49, 25.79, 24.98, 24.26, 25.63, 24.1, 23.68]. The historical stock value data of PBYI for the past 45 days is: [2.46, 2.65, 2.49, 2.78, 2.81, 2.9, 3.16, 3.31, 3.5, 3.8, 3.69, 3.8, 3.99, 3.85, 3.89, 3.85, 4.14, 4.34, 4.49, 4.1, 3.88, 3.9, 3.75, 3.8, 3.73, 3.9, 4.18, 4.13, 3.8, 3.75, 4.0, 3.9, 3.92, 3.95, 4.0, 3.93, 3.94, 4.12, 4.46, 4.34, 4.35, 4.33, 4.53, 4.39, 4.29]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_29.pkl | external_data/ground_truth_data/ground_truth_data_29.pkl | external_data/context/context_29.pkl | external_data/constraint/constraint_29.pkl |
30 | easy_stock-future volatility | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 hours. Your goal is to make the most accurate prediction .The historical stock value data of NGL for the past 19 days is: [4.11, 4.13, 4.12, 4.13, 4.11, 4.09, 4.16, 4.18, 4.14, 4.16, 4.18, 4.16, 4.21, 4.19, 4.16, 4.13, 4.17, 4.17, 4.15]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_30.pkl | external_data/ground_truth_data/ground_truth_data_30.pkl | external_data/context/context_30.pkl | external_data/constraint/constraint_30.pkl |
31 | easy_stock-future volatility | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 5 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 5 hours. Your goal is to make the most accurate prediction .The historical stock value data of SLV for the past 18 days is: [25.48, 25.44, 25.48, 25.76, 25.62, 25.81, 25.85, 25.81, 25.84, 25.84, 25.62, 25.88, 25.8, 25.81, 25.85, 25.92, 25.94, 25.74]. The historical stock value data of NULV for the past 18 days is: [39.65, 39.74, 39.65, 39.98, 39.98, 40.15, 40.18, 40.08, 40.08, 40.07, 39.94, 40.12, 39.93, 40.01, 40.05, 40.16, 40.18, 39.53]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_31.pkl | external_data/ground_truth_data/ground_truth_data_31.pkl | external_data/context/context_31.pkl | external_data/constraint/constraint_31.pkl |
32 | easy_stock-future volatility | I have the past 36 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 23 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 36 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 23 days. Your goal is to make the most accurate prediction .The historical stock value data of SES for the past 36 days is: [1.39, 1.27, 1.21, 1.23, 1.36, 1.36, 1.31, 1.36, 1.3, 1.18, 1.29, 1.22, 1.26, 1.36, 1.41, 1.32, 1.41, 1.46, 1.44, 1.4, 1.39, 1.35, 1.38, 1.49, 1.7, 1.74, 1.77, 1.68, 1.71, 1.59, 1.59, 1.64, 1.66, 1.55, 1.61, 1.54]. The historical stock value data of SPRC for the past 36 days is: [3.46, 6.16, 6.13, 4.6, 3.89, 3.91, 3.58, 3.62, 3.71, 3.67, 3.87, 3.92, 3.7, 3.55, 3.62, 3.45, 3.62, 3.54, 3.48, 4.02, 4.17, 3.86, 3.79, 3.74, 3.91, 3.74, 3.82, 3.97, 3.79, 3.63, 3.58, 3.6, 3.38, 3.19, 2.91, 2.8]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_32.pkl | external_data/ground_truth_data/ground_truth_data_32.pkl | external_data/context/context_32.pkl | external_data/constraint/constraint_32.pkl |
33 | easy_stock-future volatility | I have the past 86 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 28 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 86 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 28 days. Your goal is to make the most accurate prediction .The historical stock value data of MARPS for the past 86 days is: [4.8, 4.85, 4.87, 4.81, 4.62, 4.7, 4.83, 4.97, 4.86, 4.76, 4.9, 4.82, 4.81, 4.63, 4.76, 4.88, 4.88, 4.88, 4.88, 4.99, 4.92, 5.07, 5.05, 5.08, 4.85, 4.68, 4.58, 4.63, 4.67, 4.65, 4.94, 4.4, 4.34, 4.53, 4.27, 4.43, 3.97, 4.05, 4.14, 4.08, 4.02, 4.11, 4.16, 4.13, 4.21, 4.21, 4.21, 4.33, 4.47, 4.35, 4.22, 4.32, 4.4, 4.29, 4.21, 4.23, 4.21, 4.04, 4.1, 4.06, 4.08, 4.22, 4.13, 4.05, 3.98, 3.96, 3.99, 3.95, 3.95, 3.8, 3.81, 3.76, 3.79, 3.9, 3.99, 4.11, 4.38, 4.3, 4.33, 4.27, 4.47, 4.5, 4.48, 4.38, 4.47, 4.18]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_33.pkl | external_data/ground_truth_data/ground_truth_data_33.pkl | external_data/context/context_33.pkl | external_data/constraint/constraint_33.pkl |
34 | easy_stock-future volatility | I have the past 80 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 20 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 80 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 20 days. Your goal is to make the most accurate prediction .The historical stock value data of MNDO for the past 80 days is: [1.91, 1.89, 1.89, 1.89, 1.86, 1.89, 1.88, 1.88, 1.87, 1.88, 1.87, 1.9, 1.89, 1.9, 1.88, 1.89, 1.88, 1.89, 1.91, 1.92, 1.89, 1.88, 1.88, 1.88, 1.88, 1.89, 1.87, 1.89, 1.89, 1.89, 1.88, 1.87, 1.89, 1.85, 1.9, 1.88, 1.89, 1.86, 1.86, 1.86, 1.86, 1.85, 1.86, 1.88, 1.86, 1.86, 1.86, 1.88, 1.87, 1.87, 1.87, 1.88, 1.87, 1.86, 1.86, 1.87, 1.86, 1.88, 1.89, 1.9, 1.93, 1.93, 1.94, 1.91, 1.89, 1.94, 1.88, 1.88, 1.89, 1.89, 1.9, 1.89, 1.9, 1.9, 1.89, 1.89, 1.87, 1.86, 1.81, 1.85]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_34.pkl | external_data/ground_truth_data/ground_truth_data_34.pkl | external_data/context/context_34.pkl | external_data/constraint/constraint_34.pkl |
35 | easy_stock-future volatility | I have the past 43 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 43 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 days. Your goal is to make the most accurate prediction .The historical stock value data of RFV for the past 43 days is: [109.26, 108.36, 109.32, 109.88, 111.43, 111.44, 110.9, 112.16, 112.67, 113.37, 113.23, 110.25, 111.38, 111.05, 109.32, 109.92, 109.75, 110.74, 111.57, 113.42, 108.7, 110.05, 111.88, 110.75, 109.6, 109.97, 110.32, 110.77, 110.28, 111.28, 110.36, 111.19, 111.89, 112.0, 112.28, 112.11, 113.51, 113.55, 113.27, 113.5, 113.93, 112.19, 112.5]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_35.pkl | external_data/ground_truth_data/ground_truth_data_35.pkl | external_data/context/context_35.pkl | external_data/constraint/constraint_35.pkl |
36 | easy_stock-future volatility | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 14 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 18 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 14 hours. Your goal is to make the most accurate prediction .The historical stock value data of NEXT for the past 18 days is: [4.68, 4.64, 4.66, 4.78, 4.74, 4.77, 4.8, 4.75, 4.74, 4.73, 4.7, 4.72, 4.7, 4.86, 4.76, 4.76, 4.7, 4.71]. The historical stock value data of PWR for the past 18 days is: [251.4, 251.78, 251.47, 250.87, 251.64, 251.38, 249.26, 249.04, 248.07, 249.26, 247.68, 249.24, 247.91, 249.7, 249.2, 250.62, 250.97, 250.35]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_36.pkl | external_data/ground_truth_data/ground_truth_data_36.pkl | external_data/context/context_36.pkl | external_data/constraint/constraint_36.pkl |
37 | easy_stock-future volatility | I have the past 49 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 49 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 days. Your goal is to make the most accurate prediction .The historical stock value data of PXF for the past 49 days is: [42.34, 42.16, 42.52, 42.32, 41.6, 41.08, 41.15, 41.41, 41.81, 41.95, 42.49, 42.75, 42.38, 42.01, 42.34, 42.46, 41.85, 41.38, 41.0, 41.08, 41.15, 40.91, 40.61, 40.44, 40.97, 40.9, 41.34, 42.2, 42.61, 42.52, 42.07, 41.92, 41.92, 42.15, 42.27, 43.39, 43.33, 43.22, 43.83, 43.99, 43.81, 43.76, 44.13, 44.0, 44.13, 44.16, 44.19, 44.65, 44.26]. The historical stock value data of NOBL for the past 49 days is: [87.38, 87.11, 87.47, 87.1, 86.19, 85.54, 86.05, 85.38, 85.7, 86.27, 86.9, 86.89, 85.9, 85.86, 86.64, 87.05, 85.73, 84.75, 84.18, 83.67, 84.27, 83.84, 83.84, 82.75, 83.51, 84.06, 84.22, 85.78, 86.57, 86.26, 85.85, 85.83, 85.15, 85.92, 85.84, 87.79, 88.24, 88.1, 88.4, 88.51, 88.66, 88.94, 89.25, 88.94, 88.87, 88.73, 89.61, 90.64, 90.8]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_37.pkl | external_data/ground_truth_data/ground_truth_data_37.pkl | external_data/context/context_37.pkl | external_data/constraint/constraint_37.pkl |
38 | easy_stock-future volatility | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 5 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 5 hours. Your goal is to make the most accurate prediction .The historical stock value data of WRBY for the past 12 days is: [13.51, 13.52, 13.4, 13.55, 13.08, 13.01, 12.78, 12.88, 12.78, 13.1, 13.12, 13.81]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_38.pkl | external_data/ground_truth_data/ground_truth_data_38.pkl | external_data/context/context_38.pkl | external_data/constraint/constraint_38.pkl |
39 | easy_stock-future volatility | I have the past 18 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 8 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 18 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 8 hours. Your goal is to make the most accurate prediction .The historical stock value data of SLAB for the past 18 days is: [105.61, 105.25, 106.35, 105.45, 106.57, 105.6, 107.97, 106.59, 106.72, 106.25, 105.46, 105.73, 105.64, 105.57, 106.82, 104.68, 105.4, 106.44]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_39.pkl | external_data/ground_truth_data/ground_truth_data_39.pkl | external_data/context/context_39.pkl | external_data/constraint/constraint_39.pkl |
40 | easy_stock-future volatility | I have the past 65 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 27 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 65 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 27 days. Your goal is to make the most accurate prediction .The historical stock value data of PACB for the past 65 days is: [9.44, 9.19, 10.16, 9.76, 9.9, 10.07, 10.28, 10.09, 10.36, 9.81, 9.59, 9.21, 9.05, 9.4, 7.66, 7.68, 7.96, 7.0, 6.71, 6.42, 6.11, 6.36, 6.59, 6.88, 7.03, 6.96, 7.1, 7.02, 7.4, 7.02, 6.51, 6.76, 6.75, 6.44, 6.57, 6.32, 6.55, 6.64, 6.71, 6.08, 6.5, 6.67, 5.74, 5.29, 5.08, 5.13, 5.11, 5.23, 5.62, 5.57, 5.53, 5.19, 4.88, 4.35, 4.18, 4.59, 4.43, 4.4, 4.01, 4.24, 3.91, 3.79, 3.67, 3.64, 3.82]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_40.pkl | external_data/ground_truth_data/ground_truth_data_40.pkl | external_data/context/context_40.pkl | external_data/constraint/constraint_40.pkl |
41 | easy_stock-future volatility | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 6 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 12 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 6 hours. Your goal is to make the most accurate prediction .The historical stock value data of TPX for the past 12 days is: [50.14, 49.67, 49.44, 49.5, 49.7, 49.61, 49.54, 49.53, 49.32, 49.43, 49.54, 49.4]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_41.pkl | external_data/ground_truth_data/ground_truth_data_41.pkl | external_data/context/context_41.pkl | external_data/constraint/constraint_41.pkl |
42 | easy_stock-future volatility | I have the past 14 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 14 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 10 hours. Your goal is to make the most accurate prediction .The historical stock value data of UUP for the past 14 days is: [28.12, 28.2, 28.17, 28.18, 28.18, 28.18, 28.27, 28.3, 28.27, 28.25, 28.28, 28.28, 28.3, 28.33]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_42.pkl | external_data/ground_truth_data/ground_truth_data_42.pkl | external_data/context/context_42.pkl | external_data/constraint/constraint_42.pkl |
43 | easy_stock-future volatility | I have the past 11 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 11 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 22 hours. Your goal is to make the most accurate prediction .The historical stock value data of JVAL for the past 11 days is: [42.0, 41.89, 41.95, 42.06, 42.0, 41.99, 41.83, 41.46, 41.39, 41.44, 41.32]. The historical stock value data of WLDN for the past 11 days is: [38.8, 38.49, 38.54, 38.69, 38.81, 38.87, 38.44, 38.32, 37.83, 38.13, 37.77]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_43.pkl | external_data/ground_truth_data/ground_truth_data_43.pkl | external_data/context/context_43.pkl | external_data/constraint/constraint_43.pkl |
44 | easy_stock-future volatility | I have the past 33 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 27 days. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 33 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 27 days. Your goal is to make the most accurate prediction .The historical stock value data of LX for the past 33 days is: [2.04, 2.03, 2.0, 2.04, 2.09, 2.1, 2.01, 2.03, 1.99, 2.03, 1.98, 1.9, 1.89, 1.9, 1.96, 1.96, 2.02, 1.98, 1.84, 1.83, 1.83, 1.84, 1.81, 1.83, 1.77, 1.85, 1.78, 1.65, 1.65, 1.68, 1.72, 1.68, 1.73]. The historical stock value data of TREE for the past 33 days is: [15.72, 15.91, 15.73, 14.96, 14.35, 14.32, 14.64, 15.26, 15.28, 15.5, 15.14, 14.29, 13.61, 13.01, 13.63, 13.4, 13.31, 12.99, 12.51, 11.88, 12.17, 12.81, 12.03, 12.03, 11.64, 11.55, 11.05, 10.45, 10.51, 10.56, 11.07, 13.23, 15.0]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_44.pkl | external_data/ground_truth_data/ground_truth_data_44.pkl | external_data/context/context_44.pkl | external_data/constraint/constraint_44.pkl |
45 | easy_stock-future volatility | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 19 hours. Your goal is to make the most accurate prediction .Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the volatility of the stock price for the future 19 hours. Your goal is to make the most accurate prediction .The historical stock value data of LFVN for the past 13 days is: [8.91, 8.88, 8.81, 8.83, 8.89, 9.04, 9.03, 10.11, 9.92, 10.1, 10.12, 9.93, 10.15]. The historical stock value data of XYLD for the past 13 days is: [40.98, 40.79, 40.82, 40.78, 40.89, 40.87, 40.88, 40.68, 40.47, 40.41, 40.46, 40.42, 40.5]. Please give me your prediction, return a 1d numpy array with the predicted volatility of each stock. | external_data/executor_variables/executor_variables_45.pkl | external_data/ground_truth_data/ground_truth_data_45.pkl | external_data/context/context_45.pkl | external_data/constraint/constraint_45.pkl |
46 | easy_stock-future trend | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 15 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. The historical stock value data of KTF for the past 15 days is: [9.84, 9.84, 9.84, 9.83, 9.87, 9.85, 9.85, 9.9, 9.89, 9.91, 9.9, 9.89, 9.88, 9.89, 9.88]. The historical stock value data of KTRA for the past 15 days is: [0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.17, 0.18, 0.18, 0.18]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_46.pkl | external_data/ground_truth_data/ground_truth_data_46.pkl | external_data/context/context_46.pkl | external_data/constraint/constraint_46.pkl |
47 | easy_stock-future trend | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 17 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 13 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 17 hours. Your goal is to make the most accurate prediction. The historical stock value data of ZUMZ for the past 13 days is: [26.65, 26.36, 26.27, 26.08, 25.98, 25.51, 25.63, 29.0, 27.84, 28.19, 27.43, 27.31, 26.93]. The historical stock value data of KD for the past 13 days is: [23.19, 23.11, 23.17, 23.07, 23.04, 23.03, 23.1, 22.98, 22.66, 22.57, 22.6, 22.56, 22.72]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_47.pkl | external_data/ground_truth_data/ground_truth_data_47.pkl | external_data/context/context_47.pkl | external_data/constraint/constraint_47.pkl |
48 | easy_stock-future trend | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 hours. Your goal is to make the most accurate prediction. The historical stock value data of SAP for the past 17 days is: [212.19, 212.82, 212.45, 212.6, 212.99, 211.57, 210.77, 211.07, 211.08, 211.6, 211.35, 212.32, 212.05, 213.75, 214.48, 213.66, 214.02]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_48.pkl | external_data/ground_truth_data/ground_truth_data_48.pkl | external_data/context/context_48.pkl | external_data/constraint/constraint_48.pkl |
49 | easy_stock-future trend | I have the past 53 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 24 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 53 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 24 days. Your goal is to make the most accurate prediction. The historical stock value data of XCCC for the past 53 days is: [35.5, 35.71, 35.71, 35.78, 35.82, 35.86, 35.58, 35.77, 35.77, 35.76, 35.92, 35.8, 35.98, 36.11, 36.15, 36.22, 36.4, 36.53, 36.64, 36.64, 36.69, 36.67, 36.74, 36.77, 36.74, 36.76, 36.96, 36.86, 36.83, 36.83, 36.95, 37.09, 36.87, 36.77, 36.7, 36.64, 36.84, 36.87, 36.68, 36.51, 36.55, 36.46, 36.45, 36.51, 36.62, 36.31, 36.33, 36.14, 36.11, 35.69, 35.69, 35.69, 35.73]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_49.pkl | external_data/ground_truth_data/ground_truth_data_49.pkl | external_data/context/context_49.pkl | external_data/constraint/constraint_49.pkl |
50 | easy_stock-future trend | I have the past 14 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 17 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 14 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 17 hours. Your goal is to make the most accurate prediction. The historical stock value data of KYMR for the past 14 days is: [45.75, 45.69, 45.87, 46.25, 46.67, 45.6, 44.8, 44.43, 45.2, 45.05, 45.3, 45.4, 46.9, 46.06]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_50.pkl | external_data/ground_truth_data/ground_truth_data_50.pkl | external_data/context/context_50.pkl | external_data/constraint/constraint_50.pkl |
51 | easy_stock-future trend | I have the past 93 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 16 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 93 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 16 days. Your goal is to make the most accurate prediction. The historical stock value data of OGS for the past 93 days is: [61.09, 60.59, 59.16, 58.94, 57.46, 57.06, 56.78, 57.37, 58.46, 59.28, 58.74, 59.36, 59.46, 60.48, 59.59, 59.49, 60.01, 59.4, 57.06, 56.88, 56.96, 58.42, 58.99, 60.54, 57.35, 57.99, 59.5, 58.88, 58.7, 58.54, 59.45, 58.16, 57.17, 57.83, 58.27, 58.42, 58.3, 59.43, 60.25, 60.68, 61.43, 61.88, 61.89, 61.83, 61.0, 60.17, 60.2, 60.76, 60.99, 61.66, 61.72, 61.17, 61.26, 60.6, 62.2, 63.25, 62.36, 62.63, 62.17, 62.8, 62.45, 62.56, 63.02, 61.66, 61.46, 60.85, 60.74, 59.85, 60.23, 61.05, 63.11, 63.36, 63.23, 63.64, 63.26, 62.68, 63.34, 63.24, 63.76, 64.57, 64.15, 63.8, 63.54, 62.82, 63.01, 62.88, 62.54, 62.68, 62.88, 63.58, 63.1, 63.27, 63.48]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_51.pkl | external_data/ground_truth_data/ground_truth_data_51.pkl | external_data/context/context_51.pkl | external_data/constraint/constraint_51.pkl |
52 | easy_stock-future trend | I have the past 13 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 10 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 13 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 10 hours. Your goal is to make the most accurate prediction. The historical stock value data of WPRT for the past 13 days is: [5.49, 5.48, 5.51, 5.5, 5.52, 5.59, 5.5, 5.54, 5.5, 5.5, 5.51, 5.45, 5.49]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_52.pkl | external_data/ground_truth_data/ground_truth_data_52.pkl | external_data/context/context_52.pkl | external_data/constraint/constraint_52.pkl |
53 | easy_stock-future trend | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 6 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 6 hours. Your goal is to make the most accurate prediction. The historical stock value data of SNDL for the past 11 days is: [1.99, 1.98, 1.96, 1.97, 1.97, 1.97, 1.98, 2.12, 2.16, 2.15, 2.12]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_53.pkl | external_data/ground_truth_data/ground_truth_data_53.pkl | external_data/context/context_53.pkl | external_data/constraint/constraint_53.pkl |
54 | easy_stock-future trend | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 20 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 11 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 20 hours. Your goal is to make the most accurate prediction. The historical stock value data of LAES for the past 11 days is: [0.51, 0.48, 0.48, 0.49, 0.48, 0.5, 0.49, 0.46, 0.46, 0.47, 0.46]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_54.pkl | external_data/ground_truth_data/ground_truth_data_54.pkl | external_data/context/context_54.pkl | external_data/constraint/constraint_54.pkl |
55 | easy_stock-future trend | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. The historical stock value data of VO for the past 10 days is: [252.12, 251.99, 251.94, 250.58, 249.41, 248.8, 249.28, 248.83, 249.38, 249.01]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_55.pkl | external_data/ground_truth_data/ground_truth_data_55.pkl | external_data/context/context_55.pkl | external_data/constraint/constraint_55.pkl |
56 | easy_stock-future trend | I have the past 12 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 12 hours historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. The historical stock value data of PEGA for the past 12 days is: [69.59, 69.26, 68.96, 68.82, 68.87, 68.73, 68.96, 68.58, 69.93, 69.06, 69.48, 68.57]. The historical stock value data of PPH for the past 12 days is: [97.13, 97.1, 96.94, 96.68, 96.77, 96.67, 96.81, 96.71, 97.04, 97.06, 97.36, 97.35]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_56.pkl | external_data/ground_truth_data/ground_truth_data_56.pkl | external_data/context/context_56.pkl | external_data/constraint/constraint_56.pkl |
57 | easy_stock-future trend | I have the past 36 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 21 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 36 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 21 days. Your goal is to make the most accurate prediction. The historical stock value data of VTIP for the past 36 days is: [46.96, 46.94, 46.93, 47.02, 47.02, 46.98, 47.01, 47.08, 47.02, 47.03, 46.98, 47.02, 46.97, 46.92, 46.95, 46.95, 46.95, 47.09, 47.23, 47.12, 47.04, 47.09, 47.08, 47.08, 47.04, 47.02, 47.11, 47.09, 47.16, 47.13, 47.26, 47.22, 47.01, 46.96, 47.05, 47.04]. The historical stock value data of RDIV for the past 36 days is: [42.71, 42.38, 42.77, 41.9, 42.35, 42.51, 42.87, 42.86, 43.05, 42.77, 43.14, 42.36, 42.52, 43.06, 43.28, 42.82, 42.82, 42.22, 41.92, 41.48, 41.07, 40.79, 41.5, 41.9, 41.86, 41.67, 42.25, 42.3, 42.45, 42.47, 41.92, 42.21, 41.98, 41.28, 41.64, 41.72]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_57.pkl | external_data/ground_truth_data/ground_truth_data_57.pkl | external_data/context/context_57.pkl | external_data/constraint/constraint_57.pkl |
58 | easy_stock-future trend | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 9 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 9 hours. Your goal is to make the most accurate prediction. The historical stock value data of RARE for the past 17 days is: [55.9, 56.28, 56.1, 56.96, 56.82, 56.43, 55.55, 55.19, 54.7, 55.24, 54.94, 55.5, 55.23, 55.83, 55.92, 56.04, 55.96]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_58.pkl | external_data/ground_truth_data/ground_truth_data_58.pkl | external_data/context/context_58.pkl | external_data/constraint/constraint_58.pkl |
59 | easy_stock-future trend | I have the past 56 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 29 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 56 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 29 days. Your goal is to make the most accurate prediction. The historical stock value data of SCM for the past 56 days is: [12.22, 12.31, 12.16, 12.02, 11.8, 11.53, 11.59, 11.39, 11.28, 11.06, 11.21, 11.33, 11.41, 11.77, 12.04, 11.9, 11.82, 11.26, 11.28, 11.34, 11.48, 11.57, 11.72, 11.5, 11.54, 11.61, 11.6, 11.66, 11.72, 11.71, 11.75, 11.64, 11.7, 11.75, 11.84, 11.8, 11.65, 11.67, 11.8, 11.55, 11.59, 11.82, 11.83, 11.74, 11.83, 11.85, 11.75, 11.7, 11.77, 11.8, 11.85, 11.92, 11.89, 11.9, 11.99, 12.35]. The historical stock value data of STEW for the past 56 days is: [12.57, 12.45, 12.32, 12.18, 12.06, 11.99, 12.07, 11.88, 11.86, 11.77, 11.94, 12.14, 12.34, 12.67, 12.88, 12.66, 12.62, 12.5, 12.45, 12.62, 12.6, 12.82, 12.95, 12.89, 12.88, 12.99, 12.9, 12.93, 12.96, 13.0, 13.01, 13.03, 13.04, 13.18, 13.09, 12.98, 12.95, 12.97, 12.99, 13.11, 13.14, 13.28, 13.39, 13.43, 13.41, 13.48, 13.28, 13.33, 13.44, 13.5, 13.56, 13.49, 13.48, 13.5, 13.52, 13.51]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_59.pkl | external_data/ground_truth_data/ground_truth_data_59.pkl | external_data/context/context_59.pkl | external_data/constraint/constraint_59.pkl |
60 | easy_stock-future trend | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 10 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 hours. Your goal is to make the most accurate prediction. The historical stock value data of JTAI for the past 10 days is: [0.13, 0.13, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_60.pkl | external_data/ground_truth_data/ground_truth_data_60.pkl | external_data/context/context_60.pkl | external_data/constraint/constraint_60.pkl |
61 | easy_stock-future trend | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 8 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 19 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 8 hours. Your goal is to make the most accurate prediction. The historical stock value data of SIGA for the past 19 days is: [7.98, 7.82, 7.72, 7.71, 7.83, 7.82, 7.74, 7.71, 8.03, 8.06, 8.11, 8.06, 8.02, 7.98, 7.95, 7.83, 7.82, 7.71, 7.72]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_61.pkl | external_data/ground_truth_data/ground_truth_data_61.pkl | external_data/context/context_61.pkl | external_data/constraint/constraint_61.pkl |
62 | easy_stock-future trend | I have the past 47 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 26 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 47 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 26 days. Your goal is to make the most accurate prediction. The historical stock value data of RSPM for the past 47 days is: [29.57, 29.46, 30.5, 30.73, 30.64, 30.71, 30.7, 30.7, 30.78, 30.91, 30.93, 31.02, 31.31, 31.67, 32.12, 31.82, 31.29, 31.32, 31.66, 31.7, 31.82, 31.63, 32.43, 33.12, 33.04, 33.01, 33.39, 32.86, 33.21, 33.41, 33.62, 33.65, 33.54, 33.33, 33.39, 32.97, 32.82, 32.89, 33.05, 32.64, 32.58, 32.46, 32.39, 31.98, 31.63, 31.74, 31.77]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_62.pkl | external_data/ground_truth_data/ground_truth_data_62.pkl | external_data/context/context_62.pkl | external_data/constraint/constraint_62.pkl |
63 | easy_stock-future trend | I have the past 88 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 22 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 88 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 22 days. Your goal is to make the most accurate prediction. The historical stock value data of SNPS for the past 88 days is: [463.82, 450.72, 461.32, 459.75, 473.26, 479.92, 490.11, 494.95, 496.23, 489.67, 491.96, 492.05, 485.73, 482.37, 467.64, 467.82, 468.03, 455.26, 453.54, 457.0, 460.94, 469.44, 475.38, 478.64, 487.94, 489.94, 499.02, 506.16, 505.17, 518.83, 522.88, 538.32, 529.32, 533.45, 534.78, 541.52, 540.38, 541.03, 542.69, 543.53, 543.73, 552.46, 543.23, 545.96, 531.2, 535.71, 527.49, 534.15, 535.93, 556.02, 567.06, 568.09, 551.45, 556.27, 559.69, 558.65, 551.72, 559.96, 524.46, 520.25, 518.1, 517.41, 514.91, 498.97, 492.4, 490.18, 484.81, 499.98, 501.87, 505.18, 498.46, 494.4, 509.68, 507.87, 494.34, 517.31, 541.71, 548.9, 540.46, 536.68, 528.13, 539.9, 543.18, 533.35, 540.0, 552.05, 559.14, 542.43]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_63.pkl | external_data/ground_truth_data/ground_truth_data_63.pkl | external_data/context/context_63.pkl | external_data/constraint/constraint_63.pkl |
64 | easy_stock-future trend | I have the past 23 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 24 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 23 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 24 days. Your goal is to make the most accurate prediction. The historical stock value data of PK for the past 23 days is: [15.59, 15.66, 15.42, 15.08, 14.97, 14.67, 14.75, 14.38, 14.81, 14.82, 14.43, 14.73, 14.7, 14.53, 14.57, 14.51, 14.47, 14.46, 14.63, 14.98, 14.75, 14.73, 14.59]. The historical stock value data of NXT for the past 23 days is: [55.17, 54.28, 53.8, 56.32, 55.81, 55.65, 58.06, 58.92, 60.28, 60.53, 59.25, 57.84, 57.98, 56.7, 54.79, 51.83, 49.64, 46.59, 49.13, 46.88, 46.12, 45.32, 47.37]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_64.pkl | external_data/ground_truth_data/ground_truth_data_64.pkl | external_data/context/context_64.pkl | external_data/constraint/constraint_64.pkl |
65 | easy_stock-future trend | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 hours. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 17 hours historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 hours. Your goal is to make the most accurate prediction. The historical stock value data of LNC for the past 17 days is: [31.08, 31.22, 31.34, 31.2, 31.21, 30.69, 30.34, 29.86, 29.73, 29.45, 29.57, 29.45, 29.27, 29.14, 29.16, 29.17, 29.07]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_65.pkl | external_data/ground_truth_data/ground_truth_data_65.pkl | external_data/context/context_65.pkl | external_data/constraint/constraint_65.pkl |
66 | easy_stock-future trend | I have the past 54 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 26 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 54 days historical stock value data for 2 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 26 days. Your goal is to make the most accurate prediction. The historical stock value data of TFX for the past 54 days is: [251.09, 249.73, 250.16, 250.88, 251.57, 246.81, 247.92, 253.88, 249.91, 249.79, 249.79, 236.6, 236.65, 226.79, 225.0, 223.75, 222.1, 222.31, 224.3, 222.59, 223.81, 228.17, 223.7, 226.41, 225.86, 221.48, 217.53, 214.72, 215.8, 217.39, 219.8, 223.69, 220.26, 218.97, 215.72, 223.18, 225.47, 222.19, 216.72, 214.07, 211.23, 216.1, 216.23, 223.1, 217.56, 216.75, 213.78, 210.01, 209.45, 207.65, 206.74, 205.92, 206.63, 210.46]. The historical stock value data of SRV for the past 54 days is: [32.01, 32.04, 32.15, 32.28, 32.41, 32.25, 32.41, 32.72, 33.4, 33.58, 33.89, 34.27, 34.34, 34.51, 34.72, 35.09, 35.52, 36.08, 36.44, 36.71, 37.04, 37.15, 37.23, 37.32, 37.58, 37.88, 38.18, 38.27, 38.07, 38.27, 38.28, 38.31, 38.23, 38.29, 38.58, 38.71, 39.12, 39.2, 39.39, 40.49, 41.31, 41.96, 42.51, 42.56, 42.72, 42.61, 39.52, 36.76, 36.36, 37.16, 37.65, 39.04, 39.66, 40.5]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_66.pkl | external_data/ground_truth_data/ground_truth_data_66.pkl | external_data/context/context_66.pkl | external_data/constraint/constraint_66.pkl |
67 | easy_stock-future trend | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 14 days. Your goal is to make the most accurate prediction. The historical stock value data of TPSC for the past 37 days is: [35.84, 34.92, 35.05, 34.57, 34.25, 34.07, 33.72, 33.73, 34.11, 34.34, 34.89, 34.89, 34.6, 34.72, 34.84, 34.27, 34.34, 34.86, 35.23, 35.61, 35.68, 35.72, 36.09, 35.96, 35.95, 36.2, 36.44, 36.27, 36.28, 36.31, 36.29, 36.05, 35.55, 35.84, 35.76, 35.26, 35.62]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_67.pkl | external_data/ground_truth_data/ground_truth_data_67.pkl | external_data/context/context_67.pkl | external_data/constraint/constraint_67.pkl |
68 | easy_stock-future trend | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 days. Your goal is to make the most accurate prediction. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock.The historical stock value data is stored in variable VAL.
The final NumPy array you return should have a length equal to the number of stocks I provide, not the number of time steps. Each stock only needs one prediction result.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have the past 37 days historical stock value data for 1 stocks that I'm interested in investing in. I want to predict the trend of the stock price for the future 12 days. Your goal is to make the most accurate prediction. The historical stock value data of PNBK for the past 37 days is: [3.45, 3.32, 3.3, 3.3, 3.48, 3.54, 3.54, 3.45, 3.58, 3.29, 3.33, 2.88, 2.68, 2.4, 2.18, 2.25, 2.2, 2.13, 2.0, 1.97, 1.89, 1.98, 1.8, 2.12, 1.99, 1.91, 2.26, 2.32, 2.35, 2.45, 2.56, 2.26, 1.82, 2.01, 1.9, 1.83, 2.05]. Please give me your prediction, return a 1d numpy array with the predicted trend containing string values among (increasing, decreasing, unknown). Generate one prediction for each stock. | external_data/executor_variables/executor_variables_68.pkl | external_data/ground_truth_data/ground_truth_data_68.pkl | external_data/context/context_68.pkl | external_data/constraint/constraint_68.pkl |
69 | electricity_prediction-max_load | I have historical Temperature, Relative Humidity, Wind Speed data and the corresponding load_power data for the past 117 minutes. I need to ensure that the maximum allowable system load does not exceed 1.0689227278350713 MW. Think about how Temperature, Relative Humidity, Wind Speed influence load_power. Please give me a forecast for the next 12 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity, Wind Speed data and the corresponding load_power data for the past 117 minutes. I need to ensure that the maximum allowable system load does not exceed 1.0689227278350713 MW. The historical Temperature data for the past 117 minutes is: [24.58, 24.6, 24.62, 24.64, 24.66, 24.68, 24.7, 24.72, 24.74, 24.76, 24.78, 24.8, 24.82, 24.84, 24.86, 24.88, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.92, 24.94, 24.96, 24.98, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.02, 25.04, 25.06, 25.08, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.12, 25.14, 25.16, 25.18, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.22, 25.24, 25.26, 25.28, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.32, 25.34, 25.36, 25.38, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4, 25.4]. The historical Relative Humidity data for the past 117 minutes is: [89.42, 89.31, 89.2, 89.1, 88.99, 88.89, 88.78, 88.68, 88.57, 88.47, 88.36, 88.26, 88.15, 88.05, 87.94, 87.84, 87.73, 87.73, 87.73, 87.73, 87.73, 87.73, 87.63, 87.53, 87.42, 87.32, 87.22, 87.22, 87.22, 87.22, 87.22, 87.22, 87.12, 87.01, 86.91, 86.8, 86.7, 86.7, 86.7, 86.7, 86.7, 86.7, 86.6, 86.5, 86.39, 86.29, 86.19, 85.81, 85.43, 85.05, 84.67, 84.29, 84.19, 84.09, 83.99, 83.89, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.79, 83.69, 83.59, 83.5, 83.4, 83.3, 83.3, 83.3, 83.3, 83.3, 83.3, 83.32, 83.33, 83.35, 83.36, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.38, 83.08, 82.78, 82.47, 82.17, 81.87, 81.87, 81.87, 81.87, 81.87, 81.87]. The historical Wind Speed data for the past 117 minutes is: [1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.42, 1.44, 1.46, 1.48, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.48, 1.46, 1.44, 1.42, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4]. The historical load_power data for the past 117 minutes is: [0.92, 0.92, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]. Think about how Temperature, Relative Humidity, Wind Speed influence load_power. Please give me a forecast for the next 12 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_69.pkl | external_data/ground_truth_data/ground_truth_data_69.pkl | external_data/context/context_69.pkl | external_data/constraint/constraint_69.pkl |
70 | electricity_prediction-max_load | I have historical DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity data and the corresponding solar_power data for the past 54 minutes. I need to ensure that the maximum allowable system load does not exceed 0.5185866378517691 MW. Think about how DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity influence solar_power. Please give me a forecast for the next 19 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity data and the corresponding solar_power data for the past 54 minutes. I need to ensure that the maximum allowable system load does not exceed 0.5185866378517691 MW. The historical DNI data for the past 54 minutes is: [586.4, 589.8, 593.2, 596.6, 600.0, 603.0, 606.0, 609.0, 612.0, 615.0, 618.0, 621.0, 624.0, 627.0, 630.0, 632.6, 635.2, 637.8, 640.4, 643.0, 645.8, 648.6, 651.4, 654.2, 657.0, 659.4, 661.8, 664.2, 666.6, 669.0, 669.6, 670.2, 670.8, 671.4, 672.0, 674.2, 676.4, 678.6, 680.8, 683.0, 685.2, 687.4, 689.6, 691.8, 694.0, 695.6, 697.2, 698.8, 700.4, 702.0, 704.0, 706.0, 708.0, 710.0]. The historical Temperature data for the past 54 minutes is: [27.02, 27.04, 27.06, 27.08, 27.1, 27.12, 27.14, 27.16, 27.18, 27.2, 27.24, 27.28, 27.32, 27.36, 27.4, 27.42, 27.44, 27.46, 27.48, 27.5, 27.52, 27.54, 27.56, 27.58, 27.6, 27.62, 27.64, 27.66, 27.68, 27.7, 27.72, 27.74, 27.76, 27.78, 27.8, 27.82, 27.84, 27.86, 27.88, 27.9, 27.94, 27.98, 28.02, 28.06, 28.1, 28.12, 28.14, 28.16, 28.18, 28.2, 28.22, 28.24, 28.26, 28.28]. The historical Solar Zenith Angle data for the past 54 minutes is: [70.98, 70.77, 70.57, 70.36, 70.15, 69.94, 69.74, 69.53, 69.33, 69.12, 68.91, 68.71, 68.5, 68.3, 68.09, 67.89, 67.68, 67.48, 67.27, 67.07, 66.87, 66.66, 66.46, 66.25, 66.05, 65.85, 65.65, 65.44, 65.24, 65.04, 64.84, 64.64, 64.43, 64.23, 64.03, 63.83, 63.63, 63.43, 63.23, 63.03, 62.83, 62.63, 62.44, 62.24, 62.04, 61.84, 61.65, 61.45, 61.26, 61.06, 60.86, 60.67, 60.47, 60.28]. The historical Dew Point data for the past 54 minutes is: [22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.22, 22.14, 22.06, 21.98, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.92, 21.94, 21.96, 21.98]. The historical Relative Humidity data for the past 54 minutes is: [75.33, 75.24, 75.16, 75.07, 74.98, 74.89, 74.81, 74.72, 74.64, 74.55, 74.38, 74.2, 74.03, 73.85, 73.68, 73.59, 73.51, 73.42, 73.34, 73.25, 73.17, 73.08, 73.0, 72.91, 72.83, 72.75, 72.66, 72.58, 72.49, 72.41, 72.03, 71.65, 71.28, 70.9, 70.52, 70.44, 70.36, 70.27, 70.19, 70.11, 69.95, 69.79, 69.62, 69.46, 69.3, 69.22, 69.14, 69.06, 68.98, 68.9, 68.82, 68.74, 68.66, 68.58]. The historical solar_power data for the past 54 minutes is: [0.26, 0.26, 0.27, 0.27, 0.27, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.34, 0.34, 0.35, 0.35, 0.35, 0.35, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.4, 0.41, 0.41, 0.41, 0.42, 0.42, 0.42, 0.42, 0.43, 0.43]. Think about how DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity influence solar_power. Please give me a forecast for the next 19 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DNI, Temperature, Solar Zenith Angle, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_70.pkl | external_data/ground_truth_data/ground_truth_data_70.pkl | external_data/context/context_70.pkl | external_data/constraint/constraint_70.pkl |
71 | electricity_prediction-max_load | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 119 minutes. I need to ensure that the maximum allowable system load does not exceed 0.008634459597211808 MW. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 54 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 119 minutes. I need to ensure that the maximum allowable system load does not exceed 0.008634459597211808 MW. The historical Wind Speed data for the past 119 minutes is: [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.98, 1.96, 1.94, 1.92, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.88, 1.86, 1.84, 1.82, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.78, 1.76, 1.74, 1.72, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.68, 1.66, 1.64, 1.62, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.58, 1.56, 1.54, 1.52, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.48, 1.46, 1.44, 1.42, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.38, 1.36, 1.34, 1.32, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3]. The historical Relative Humidity data for the past 119 minutes is: [60.95, 60.87, 60.8, 60.66, 60.51, 60.37, 60.22, 60.08, 59.94, 59.79, 59.65, 59.5, 59.36, 59.22, 59.08, 58.93, 58.79, 58.65, 58.51, 58.37, 58.24, 58.1, 57.96, 58.09, 58.22, 58.34, 58.47, 58.6, 58.46, 58.32, 58.18, 58.04, 57.9, 57.76, 57.63, 57.49, 57.36, 57.22, 57.15, 57.08, 57.02, 56.95, 56.88, 56.74, 56.61, 56.47, 56.34, 56.2, 56.07, 55.94, 55.8, 55.67, 55.54, 55.41, 55.28, 55.15, 55.02, 54.89, 54.82, 54.76, 54.69, 54.63, 54.56, 54.43, 54.3, 54.18, 54.05, 53.92, 53.79, 53.67, 53.54, 53.42, 53.29, 53.23, 53.17, 53.1, 53.04, 52.98, 52.84, 52.71, 52.57, 52.44, 52.3, 52.47, 52.65, 52.82, 53.0, 53.17, 53.11, 53.05, 52.98, 52.92, 52.86, 52.74, 52.62, 52.49, 52.37, 52.25, 52.13, 52.01, 51.88, 51.76, 51.64, 51.58, 51.52, 51.46, 51.4, 51.34, 51.22, 51.1, 50.98, 50.86, 50.74, 50.68, 50.62, 50.56, 50.5, 50.44, 50.38]. The historical wind_power data for the past 119 minutes is: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 54 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_71.pkl | external_data/ground_truth_data/ground_truth_data_71.pkl | external_data/context/context_71.pkl | external_data/constraint/constraint_71.pkl |
72 | electricity_prediction-max_load | I have historical DHI, GHI, Dew Point, Temperature, DNI data and the corresponding solar_power data for the past 145 minutes. I need to ensure that the maximum allowable system load does not exceed 0.4242252726724446 MW. Think about how DHI, GHI, Dew Point, Temperature, DNI influence solar_power. Please give me a forecast for the next 62 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, GHI, Dew Point, Temperature, DNI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical DHI, GHI, Dew Point, Temperature, DNI data and the corresponding solar_power data for the past 145 minutes. I need to ensure that the maximum allowable system load does not exceed 0.4242252726724446 MW. The historical DHI data for the past 145 minutes is: [12.0, 17.6, 23.2, 28.8, 34.4, 40.0, 36.6, 33.2, 29.8, 26.4, 23.0, 32.6, 42.2, 51.8, 61.4, 71.0, 62.6, 54.2, 45.8, 37.4, 29.0, 30.4, 31.8, 33.2, 34.6, 36.0, 32.4, 28.8, 25.2, 21.6, 18.0, 23.0, 28.0, 33.0, 38.0, 43.0, 44.2, 45.4, 46.6, 47.8, 49.0, 42.2, 35.4, 28.6, 21.8, 15.0, 15.2, 15.4, 15.6, 15.8, 16.0, 25.8, 35.6, 45.4, 55.2, 65.0, 58.4, 51.8, 45.2, 38.6, 32.0, 29.4, 26.8, 24.2, 21.6, 19.0, 29.8, 40.6, 51.4, 62.2, 73.0, 62.8, 52.6, 42.4, 32.2, 22.0, 29.2, 36.4, 43.6, 50.8, 58.0, 74.4, 90.8, 107.2, 123.6, 140.0, 121.4, 102.8, 84.2, 65.6, 47.0, 60.2, 73.4, 86.6, 99.8, 113.0, 107.0, 101.0, 95.0, 89.0, 83.0, 84.4, 85.8, 87.2, 88.6, 90.0, 120.6, 151.2, 181.8, 212.4, 243.0, 225.2, 207.4, 189.6, 171.8, 154.0, 161.8, 169.6, 177.4, 185.2, 193.0, 206.4, 219.8, 233.2, 246.6, 260.0, 259.8, 259.6, 259.4, 259.2, 259.0, 265.8, 272.6, 279.4, 286.2, 293.0, 284.8, 276.6, 268.4, 260.2, 252.0, 250.8, 249.6, 248.4, 247.2]. The historical GHI data for the past 145 minutes is: [12.0, 17.6, 23.2, 28.8, 34.4, 40.0, 36.6, 33.2, 29.8, 26.4, 23.0, 32.6, 42.2, 51.8, 61.4, 71.0, 62.6, 54.2, 45.8, 37.4, 29.0, 30.4, 31.8, 33.2, 34.6, 36.0, 32.4, 28.8, 25.2, 21.6, 18.0, 23.0, 28.0, 33.0, 38.0, 43.0, 44.2, 45.4, 46.6, 47.8, 49.0, 42.2, 35.4, 28.6, 21.8, 15.0, 15.2, 15.4, 15.6, 15.8, 16.0, 25.8, 35.6, 45.4, 55.2, 65.0, 58.4, 51.8, 45.2, 38.6, 32.0, 29.4, 26.8, 24.2, 21.6, 19.0, 29.8, 40.6, 51.4, 62.2, 73.0, 62.8, 52.6, 42.4, 32.2, 22.0, 29.2, 36.4, 43.6, 50.8, 58.0, 74.4, 90.8, 107.2, 123.6, 140.0, 121.4, 102.8, 84.2, 65.6, 47.0, 60.2, 73.4, 86.6, 99.8, 113.0, 107.0, 101.0, 95.0, 89.0, 83.0, 84.4, 85.8, 87.2, 88.6, 90.0, 125.2, 160.4, 195.6, 230.8, 266.0, 243.6, 221.2, 198.8, 176.4, 154.0, 162.6, 171.2, 179.8, 188.4, 197.0, 214.8, 232.6, 250.4, 268.2, 286.0, 285.2, 284.4, 283.6, 282.8, 282.0, 294.0, 306.0, 318.0, 330.0, 342.0, 327.4, 312.8, 298.2, 283.6, 269.0, 267.2, 265.4, 263.6, 261.8]. The historical Dew Point data for the past 145 minutes is: [19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 19.76, 19.72, 19.68, 19.64, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6]. The historical Temperature data for the past 145 minutes is: [21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.32, 21.34, 21.36, 21.38, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.42, 21.44, 21.46, 21.48, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.52, 21.54, 21.56, 21.58, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.62, 21.64, 21.66, 21.68, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.72, 21.74, 21.76, 21.78, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.82, 21.84, 21.86, 21.88, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.92, 21.94, 21.96, 21.98]. The historical DNI data for the past 145 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.6, 15.2, 22.8, 30.4, 38.0, 30.4, 22.8, 15.2, 7.6, 0.0, 1.4, 2.8, 4.2, 5.6, 7.0, 13.6, 20.2, 26.8, 33.4, 40.0, 39.0, 38.0, 37.0, 36.0, 35.0, 42.8, 50.6, 58.4, 66.2, 74.0, 64.0, 54.0, 44.0, 34.0, 24.0, 23.2, 22.4, 21.6, 20.8]. The historical solar_power data for the past 145 minutes is: [0.0, 0.01, 0.01, 0.02, 0.02, 0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.02, 0.03, 0.04, 0.04, 0.05, 0.04, 0.04, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.03, 0.04, 0.05, 0.04, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.02, 0.03, 0.03, 0.04, 0.05, 0.04, 0.04, 0.03, 0.02, 0.01, 0.02, 0.02, 0.03, 0.03, 0.04, 0.05, 0.06, 0.08, 0.09, 0.1, 0.09, 0.07, 0.06, 0.05, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.08, 0.07, 0.07, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.19, 0.17, 0.15, 0.13, 0.11, 0.12, 0.13, 0.13, 0.14, 0.15, 0.16, 0.18, 0.19, 0.21, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.26, 0.24, 0.23, 0.22, 0.2, 0.2, 0.2, 0.2, 0.2]. Think about how DHI, GHI, Dew Point, Temperature, DNI influence solar_power. Please give me a forecast for the next 62 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, GHI, Dew Point, Temperature, DNI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_72.pkl | external_data/ground_truth_data/ground_truth_data_72.pkl | external_data/context/context_72.pkl | external_data/constraint/constraint_72.pkl |
73 | electricity_prediction-max_load | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 179 minutes. I need to ensure that the maximum allowable system load does not exceed 0.022258379478569908 MW. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 59 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 179 minutes. I need to ensure that the maximum allowable system load does not exceed 0.022258379478569908 MW. The historical Relative Humidity data for the past 179 minutes is: [52.74, 52.74, 52.74, 52.74, 52.74, 52.69, 52.64, 52.6, 52.55, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.5, 52.44, 52.38, 52.32, 52.26, 52.2, 52.2, 52.2, 52.2, 52.2, 52.2, 52.14, 52.08, 52.03, 51.97, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.91, 51.85, 51.79, 51.73, 51.67, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.61, 51.56, 51.51, 51.45, 51.4, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.35, 51.29, 51.23, 51.18, 51.12, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.06, 51.0, 50.94, 50.89, 50.83, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.77, 50.88, 50.99, 51.11, 51.22, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.33, 51.27, 51.21, 51.16, 51.1, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 51.04, 50.98, 50.92, 50.87, 50.81, 50.75, 50.75, 50.75, 50.75, 50.75, 50.75, 50.75, 50.75, 50.75, 50.75]. The historical Wind Speed data for the past 179 minutes is: [2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.18, 2.16, 2.14, 2.12, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.08, 2.06, 2.04, 2.02, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.98, 1.96, 1.94, 1.92, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.88, 1.86, 1.84, 1.82, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8]. The historical wind_power data for the past 179 minutes is: [0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 59 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_73.pkl | external_data/ground_truth_data/ground_truth_data_73.pkl | external_data/context/context_73.pkl | external_data/constraint/constraint_73.pkl |
74 | electricity_prediction-max_load | I have historical Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity data and the corresponding solar_power data for the past 113 minutes. I need to ensure that the maximum allowable system load does not exceed 0.5687688990012139 MW. Think about how Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity influence solar_power. Please give me a forecast for the next 57 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity data and the corresponding solar_power data for the past 113 minutes. I need to ensure that the maximum allowable system load does not exceed 0.5687688990012139 MW. The historical Solar Zenith Angle data for the past 113 minutes is: [91.05, 90.75, 90.44, 90.14, 89.98, 89.82, 89.67, 89.51, 89.35, 89.19, 89.02, 88.86, 88.69, 88.53, 88.36, 88.19, 88.03, 87.86, 87.69, 87.52, 87.35, 87.17, 87.0, 86.83, 86.66, 86.48, 86.31, 86.13, 85.96, 85.79, 85.61, 85.44, 85.26, 85.09, 84.92, 84.74, 84.57, 84.39, 84.22, 84.05, 83.87, 83.7, 83.52, 83.35, 83.18, 83.01, 82.83, 82.66, 82.49, 82.32, 82.15, 81.98, 81.81, 81.64, 81.47, 81.3, 81.13, 80.96, 80.79, 80.62, 80.45, 80.28, 80.11, 79.94, 79.77, 79.61, 79.44, 79.28, 79.11, 78.94, 78.78, 78.61, 78.45, 78.28, 78.12, 77.95, 77.79, 77.62, 77.46, 77.3, 77.14, 76.97, 76.81, 76.65, 76.49, 76.33, 76.17, 76.01, 75.85, 75.69, 75.53, 75.38, 75.22, 75.06, 74.9, 74.75, 74.59, 74.44, 74.28, 74.13, 73.97, 73.82, 73.66, 73.51, 73.36, 73.21, 73.05, 72.9, 72.75, 72.6, 72.45, 72.3, 72.15]. The historical Temperature data for the past 113 minutes is: [12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.82, 12.84, 12.86, 12.88, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.92, 12.94, 12.96, 12.98, 13.0, 13.02, 13.04, 13.06, 13.08, 13.1, 13.12, 13.14, 13.16, 13.18, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.22, 13.24, 13.26, 13.28, 13.3, 13.32, 13.34, 13.36, 13.38, 13.4, 13.42, 13.44, 13.46, 13.48, 13.5, 13.52, 13.54, 13.56, 13.58, 13.6, 13.62, 13.64, 13.66, 13.68, 13.7, 13.72, 13.74, 13.76, 13.78, 13.8, 13.82, 13.84, 13.86, 13.88, 13.9, 13.9, 13.9, 13.9, 13.9, 13.9, 13.94, 13.98, 14.02, 14.06, 14.1, 14.12, 14.14, 14.16, 14.18, 14.2, 14.22, 14.24, 14.26, 14.28, 14.3, 14.32, 14.34, 14.36, 14.38, 14.4, 14.44, 14.48, 14.52, 14.56, 14.6, 14.62, 14.64, 14.66, 14.68, 14.7, 14.72, 14.74, 14.76, 14.78]. The historical DHI data for the past 113 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 1.2, 1.8, 2.4, 3.0, 4.2, 5.4, 6.6, 7.8, 9.0, 9.8, 10.6, 11.4, 12.2, 13.0, 13.8, 14.6, 15.4, 16.2, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 24.6, 27.2, 29.8, 32.4, 35.0, 36.2, 37.4, 38.6, 39.8, 41.0, 42.2, 43.4, 44.6, 45.8, 47.0, 48.2, 49.4, 50.6, 51.8, 53.0, 54.2, 55.4, 56.6, 57.8, 59.0, 58.6, 58.2, 57.8, 57.4, 57.0, 58.4, 59.8, 61.2, 62.6, 64.0, 65.4, 66.8, 68.2, 69.6, 71.0, 72.2, 73.4, 74.6, 75.8, 77.0, 78.2, 79.4, 80.6, 81.8, 83.0, 84.2, 85.4, 86.6, 87.8, 89.0, 90.2, 91.4, 92.6, 93.8, 95.0, 84.2, 73.4, 62.6, 51.8, 41.0, 53.0, 65.0, 77.0, 89.0, 101.0, 104.0, 107.0, 110.0, 113.0, 116.0, 115.0, 114.0, 113.0, 112.0]. The historical DNI data for the past 113 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 13.6, 27.2, 40.8, 54.4, 68.0, 54.4, 40.8, 27.2, 13.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0, 16.0, 24.0, 32.0, 40.0, 41.4, 42.8, 44.2, 45.6, 47.0, 48.2, 49.4, 50.6, 51.8, 53.0, 54.2, 55.4, 56.6, 57.8, 59.0, 60.2, 61.4, 62.6, 63.8, 65.0, 52.0, 39.0, 26.0, 13.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 1.6, 2.4, 3.2, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 15.2, 11.4, 7.6, 3.8, 0.0, 0.6, 1.2, 1.8, 2.4, 3.0, 45.2, 87.4, 129.6, 171.8, 214.0, 235.8, 257.6, 279.4, 301.2]. The historical Relative Humidity data for the past 113 minutes is: [89.29, 89.29, 89.29, 89.29, 89.29, 89.29, 89.29, 89.29, 89.29, 89.17, 89.06, 88.94, 88.83, 88.71, 88.71, 88.71, 88.71, 88.71, 88.71, 88.59, 88.48, 88.36, 88.25, 88.13, 88.02, 87.9, 87.79, 87.67, 87.56, 87.45, 87.33, 87.22, 87.1, 86.99, 86.99, 86.99, 86.99, 86.99, 86.99, 86.88, 86.77, 86.65, 86.54, 86.43, 86.32, 86.21, 86.09, 85.98, 85.87, 85.76, 85.65, 85.55, 85.44, 85.33, 85.22, 85.11, 85.0, 84.89, 84.78, 84.67, 84.56, 84.45, 84.34, 84.23, 84.14, 84.05, 83.95, 83.86, 83.77, 83.66, 83.55, 83.45, 83.34, 83.23, 83.23, 83.23, 83.23, 83.23, 83.23, 83.02, 82.8, 82.59, 82.37, 82.16, 82.05, 81.95, 81.84, 81.74, 81.63, 81.53, 81.42, 81.32, 81.21, 81.11, 81.0, 80.9, 80.79, 80.69, 80.58, 80.37, 80.17, 79.96, 79.76, 79.55, 79.45, 79.35, 79.24, 79.14, 79.04, 78.97, 78.9, 78.82, 78.75]. The historical solar_power data for the past 113 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.06, 0.06, 0.05, 0.05, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.1, 0.13, 0.15, 0.18, 0.2, 0.21, 0.22, 0.23, 0.24]. Think about how Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity influence solar_power. Please give me a forecast for the next 57 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Temperature, DHI, DNI, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_74.pkl | external_data/ground_truth_data/ground_truth_data_74.pkl | external_data/context/context_74.pkl | external_data/constraint/constraint_74.pkl |
75 | electricity_prediction-max_load | I have historical Temperature, Solar Zenith Angle, Dew Point data and the corresponding load_power data for the past 167 minutes. I need to ensure that the maximum allowable system load does not exceed 1.0900825368469065 MW. Think about how Temperature, Solar Zenith Angle, Dew Point influence load_power. Please give me a forecast for the next 43 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Solar Zenith Angle, Dew Point are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Solar Zenith Angle, Dew Point data and the corresponding load_power data for the past 167 minutes. I need to ensure that the maximum allowable system load does not exceed 1.0900825368469065 MW. The historical Temperature data for the past 167 minutes is: [-4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.28, -4.26, -4.24, -4.22, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.2, -4.22, -4.24, -4.26, -4.28, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.3, -4.32, -4.34, -4.36, -4.38, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.42, -4.44, -4.46, -4.48, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.52, -4.54, -4.56, -4.58, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.6, -4.62, -4.64, -4.66, -4.68, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.7, -4.72, -4.74, -4.76, -4.78, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.8, -4.82, -4.84, -4.86, -4.88, -4.9, -4.9]. The historical Solar Zenith Angle data for the past 167 minutes is: [143.11, 143.13, 143.15, 143.16, 143.18, 143.2, 143.21, 143.22, 143.23, 143.24, 143.25, 143.26, 143.26, 143.27, 143.27, 143.28, 143.28, 143.27, 143.27, 143.26, 143.26, 143.25, 143.24, 143.24, 143.23, 143.22, 143.2, 143.19, 143.17, 143.16, 143.14, 143.12, 143.1, 143.07, 143.05, 143.03, 143.0, 142.97, 142.94, 142.91, 142.88, 142.84, 142.81, 142.77, 142.74, 142.7, 142.66, 142.62, 142.57, 142.53, 142.49, 142.44, 142.39, 142.35, 142.3, 142.25, 142.2, 142.14, 142.09, 142.03, 141.98, 141.92, 141.86, 141.79, 141.73, 141.67, 141.6, 141.54, 141.47, 141.41, 141.34, 141.27, 141.2, 141.12, 141.05, 140.98, 140.9, 140.82, 140.75, 140.67, 140.59, 140.51, 140.42, 140.34, 140.25, 140.17, 140.08, 139.99, 139.91, 139.82, 139.73, 139.64, 139.54, 139.45, 139.35, 139.26, 139.16, 139.06, 138.97, 138.87, 138.77, 138.67, 138.56, 138.46, 138.35, 138.25, 138.14, 138.03, 137.93, 137.82, 137.71, 137.6, 137.49, 137.37, 137.26, 137.15, 137.03, 136.92, 136.8, 136.69, 136.57, 136.45, 136.33, 136.21, 136.09, 135.97, 135.85, 135.72, 135.6, 135.47, 135.35, 135.22, 135.09, 134.97, 134.84, 134.71, 134.58, 134.45, 134.31, 134.18, 134.05, 133.92, 133.78, 133.65, 133.51, 133.38, 133.24, 133.1, 132.97, 132.83, 132.69, 132.55, 132.41, 132.27, 132.13, 131.99, 131.85, 131.7, 131.56, 131.41, 131.27, 131.12, 130.98, 130.83, 130.69, 130.54, 130.39]. The historical Dew Point data for the past 167 minutes is: [-6.0, -6.0, -6.0, -6.0, -6.0, -6.0, -6.04, -6.08, -6.12, -6.16, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.2, -6.34, -6.48, -6.62, -6.76, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -6.9, -7.06, -7.22, -7.38, -7.54, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7, -7.7]. The historical load_power data for the past 167 minutes is: [0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99]. Think about how Temperature, Solar Zenith Angle, Dew Point influence load_power. Please give me a forecast for the next 43 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Solar Zenith Angle, Dew Point are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_75.pkl | external_data/ground_truth_data/ground_truth_data_75.pkl | external_data/context/context_75.pkl | external_data/constraint/constraint_75.pkl |
76 | electricity_prediction-max_load | I have historical Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI data and the corresponding solar_power data for the past 79 minutes. I need to ensure that the maximum allowable system load does not exceed 0.7133530404420658 MW. Think about how Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI influence solar_power. Please give me a forecast for the next 45 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI data and the corresponding solar_power data for the past 79 minutes. I need to ensure that the maximum allowable system load does not exceed 0.7133530404420658 MW. The historical Solar Zenith Angle data for the past 79 minutes is: [14.6, 14.59, 14.58, 14.58, 14.57, 14.56, 14.57, 14.57, 14.58, 14.58, 14.59, 14.62, 14.64, 14.67, 14.69, 14.72, 14.76, 14.8, 14.85, 14.89, 14.93, 14.99, 15.05, 15.1, 15.16, 15.22, 15.29, 15.37, 15.44, 15.52, 15.59, 15.68, 15.76, 15.85, 15.93, 16.02, 16.12, 16.22, 16.32, 16.42, 16.52, 16.63, 16.75, 16.86, 16.98, 17.09, 17.21, 17.33, 17.46, 17.58, 17.7, 17.83, 17.96, 18.1, 18.23, 18.36, 18.5, 18.64, 18.79, 18.93, 19.07, 19.22, 19.37, 19.52, 19.67, 19.82, 19.98, 20.13, 20.29, 20.44, 20.6, 20.76, 20.92, 21.09, 21.25, 21.41, 21.58, 21.74, 21.91]. The historical DNI data for the past 79 minutes is: [916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 916.0, 915.6, 915.2, 914.8, 914.4, 914.0, 913.8, 913.6, 913.4, 913.2, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 913.0, 912.8, 912.6, 912.4, 912.2, 912.0, 912.0, 912.0, 912.0, 912.0, 912.0, 911.8, 911.6, 911.4, 911.2, 911.0, 910.8, 910.6, 910.4, 910.2, 910.0, 909.4, 908.8, 908.2, 907.6, 907.0, 906.8, 906.6, 906.4, 906.2, 906.0, 905.8, 905.6, 905.4, 905.2, 905.0, 904.8, 904.6, 904.4, 904.2, 904.0, 903.8, 903.6, 903.4, 903.2, 903.0, 902.6, 902.2, 901.8]. The historical Relative Humidity data for the past 79 minutes is: [28.29, 28.29, 28.29, 28.29, 28.29, 28.29, 28.26, 28.23, 28.19, 28.16, 28.13, 28.1, 28.07, 28.04, 28.01, 27.98, 27.95, 27.92, 27.89, 27.86, 27.83, 27.83, 27.83, 27.83, 27.83, 27.83, 27.8, 27.77, 27.73, 27.7, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.67, 27.63, 27.6, 27.56, 27.53, 27.49, 27.49, 27.49, 27.49, 27.49, 27.49, 27.51, 27.53, 27.55, 27.57, 27.59, 27.59, 27.59, 27.59, 27.59, 27.59, 27.56, 27.53, 27.5, 27.47, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.44, 27.41, 27.38, 27.35]. The historical Dew Point data for the past 79 minutes is: [13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.7, 13.72, 13.74, 13.76, 13.78, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8, 13.8]. The historical DHI data for the past 79 minutes is: [114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 113.8, 113.6, 113.4, 113.2, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 113.2, 113.4, 113.6, 113.8, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 113.8, 113.6, 113.4, 113.2, 113.0, 113.0, 113.0, 113.0]. The historical solar_power data for the past 79 minutes is: [0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69]. Think about how Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI influence solar_power. Please give me a forecast for the next 45 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, DNI, Relative Humidity, Dew Point, DHI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_76.pkl | external_data/ground_truth_data/ground_truth_data_76.pkl | external_data/context/context_76.pkl | external_data/constraint/constraint_76.pkl |
77 | electricity_prediction-max_load | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 169 minutes. I need to ensure that the maximum allowable system load does not exceed 0.4580154876951332 MW. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 65 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 169 minutes. I need to ensure that the maximum allowable system load does not exceed 0.4580154876951332 MW. The historical Wind Speed data for the past 169 minutes is: [4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.22, 4.24, 4.26, 4.28, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.32, 4.34, 4.36, 4.38, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.42, 4.44, 4.46, 4.48, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.52, 4.54, 4.56, 4.58, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.62, 4.64, 4.66, 4.68, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7]. The historical Temperature data for the past 169 minutes is: [24.94, 24.92, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.88, 24.86, 24.84, 24.82, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.78, 24.76, 24.74, 24.72, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.68, 24.66, 24.64, 24.62, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.58, 24.56, 24.54, 24.52, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.48, 24.46, 24.44, 24.42, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.38, 24.36, 24.34, 24.32, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.28, 24.26, 24.24, 24.22, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.18, 24.16, 24.14, 24.12, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.1, 24.08, 24.06, 24.04, 24.02, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0]. The historical wind_power data for the past 169 minutes is: [0.32, 0.32, 0.33, 0.33, 0.32, 0.32, 0.32, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.33, 0.34, 0.34, 0.34, 0.35, 0.35, 0.35, 0.35, 0.35, 0.36, 0.35, 0.35, 0.34, 0.34, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.34, 0.34, 0.35, 0.35, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.35, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.37, 0.37, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.37, 0.37, 0.36, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39, 0.38, 0.38, 0.37, 0.37, 0.37, 0.36, 0.36, 0.35, 0.36, 0.37, 0.37, 0.38, 0.38, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.39, 0.39, 0.39, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.41, 0.4, 0.4, 0.39, 0.39, 0.39, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.4, 0.39, 0.39, 0.39, 0.39, 0.4, 0.4, 0.41, 0.41, 0.42, 0.42, 0.43, 0.43, 0.44, 0.43, 0.43, 0.43, 0.42, 0.42, 0.42]. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 65 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_77.pkl | external_data/ground_truth_data/ground_truth_data_77.pkl | external_data/context/context_77.pkl | external_data/constraint/constraint_77.pkl |
78 | electricity_prediction-max_load | I have historical Temperature, Wind Speed, Solar Zenith Angle data and the corresponding load_power data for the past 121 minutes. I need to ensure that the maximum allowable system load does not exceed 1.102318138057357 MW. Think about how Temperature, Wind Speed, Solar Zenith Angle influence load_power. Please give me a forecast for the next 51 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Wind Speed, Solar Zenith Angle data and the corresponding load_power data for the past 121 minutes. I need to ensure that the maximum allowable system load does not exceed 1.102318138057357 MW. The historical Temperature data for the past 121 minutes is: [28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 27.98, 27.96, 27.94, 27.92, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.9, 27.88, 27.86, 27.84, 27.82, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.8, 27.78, 27.76, 27.74, 27.72, 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.68, 27.66, 27.64, 27.62, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.58, 27.56, 27.54, 27.52, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.48, 27.46, 27.44, 27.42, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4, 27.4]. The historical Wind Speed data for the past 121 minutes is: [4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.68, 4.66, 4.64, 4.62, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.58, 4.56, 4.54, 4.52, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.48, 4.46, 4.44, 4.42, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.38, 4.36, 4.34, 4.32, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.28, 4.26, 4.24, 4.22, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.18, 4.16, 4.14, 4.12, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1]. The historical Solar Zenith Angle data for the past 121 minutes is: [43.16, 43.36, 43.56, 43.76, 43.96, 44.17, 44.37, 44.57, 44.77, 44.97, 45.18, 45.38, 45.58, 45.78, 45.99, 46.19, 46.4, 46.6, 46.8, 47.0, 47.21, 47.41, 47.61, 47.81, 48.02, 48.22, 48.43, 48.63, 48.83, 49.04, 49.24, 49.45, 49.65, 49.85, 50.06, 50.26, 50.47, 50.67, 50.87, 51.08, 51.28, 51.49, 51.69, 51.89, 52.09, 52.3, 52.5, 52.7, 52.9, 53.11, 53.31, 53.52, 53.72, 53.92, 54.13, 54.33, 54.54, 54.74, 54.94, 55.15, 55.35, 55.56, 55.76, 55.96, 56.17, 56.37, 56.58, 56.78, 56.98, 57.18, 57.39, 57.59, 57.79, 57.99, 58.2, 58.4, 58.61, 58.81, 59.01, 59.22, 59.42, 59.63, 59.83, 60.03, 60.23, 60.44, 60.64, 60.84, 61.04, 61.24, 61.45, 61.65, 61.85, 62.05, 62.26, 62.46, 62.67, 62.87, 63.07, 63.27, 63.48, 63.68, 63.88, 64.08, 64.28, 64.48, 64.68, 64.88, 65.08, 65.28, 65.49, 65.69, 65.89, 66.09, 66.29, 66.5, 66.7, 66.9, 67.1, 67.3, 67.5]. The historical load_power data for the past 121 minutes is: [1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.06, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08]. Think about how Temperature, Wind Speed, Solar Zenith Angle influence load_power. Please give me a forecast for the next 51 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_78.pkl | external_data/ground_truth_data/ground_truth_data_78.pkl | external_data/context/context_78.pkl | external_data/constraint/constraint_78.pkl |
79 | electricity_prediction-max_load | I have historical Solar Zenith Angle, Dew Point, Relative Humidity data and the corresponding load_power data for the past 127 minutes. I need to ensure that the maximum allowable system load does not exceed 1.2445746664275639 MW. Think about how Solar Zenith Angle, Dew Point, Relative Humidity influence load_power. Please give me a forecast for the next 37 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Solar Zenith Angle, Dew Point, Relative Humidity data and the corresponding load_power data for the past 127 minutes. I need to ensure that the maximum allowable system load does not exceed 1.2445746664275639 MW. The historical Solar Zenith Angle data for the past 127 minutes is: [59.58, 59.77, 59.96, 60.15, 60.33, 60.52, 60.7, 60.89, 61.08, 61.27, 61.45, 61.64, 61.83, 62.02, 62.21, 62.39, 62.58, 62.77, 62.96, 63.15, 63.33, 63.52, 63.71, 63.9, 64.08, 64.27, 64.45, 64.64, 64.83, 65.02, 65.2, 65.39, 65.58, 65.77, 65.96, 66.14, 66.33, 66.52, 66.71, 66.89, 67.08, 67.26, 67.45, 67.64, 67.83, 68.01, 68.2, 68.39, 68.58, 68.76, 68.95, 69.13, 69.32, 69.51, 69.7, 69.88, 70.07, 70.26, 70.45, 70.63, 70.82, 71.0, 71.19, 71.38, 71.56, 71.75, 71.93, 72.12, 72.3, 72.49, 72.67, 72.86, 73.04, 73.23, 73.41, 73.6, 73.78, 73.97, 74.15, 74.34, 74.52, 74.71, 74.89, 75.07, 75.26, 75.44, 75.63, 75.81, 75.99, 76.18, 76.36, 76.55, 76.73, 76.91, 77.09, 77.28, 77.46, 77.64, 77.82, 78.0, 78.19, 78.37, 78.55, 78.73, 78.91, 79.1, 79.28, 79.46, 79.64, 79.82, 80.0, 80.18, 80.36, 80.54, 80.72, 80.9, 81.08, 81.26, 81.44, 81.62, 81.8, 81.98, 82.16, 82.34, 82.51, 82.69, 82.86]. The historical Dew Point data for the past 127 minutes is: [17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.62, 17.84, 18.06, 18.28, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.28, 18.06, 17.84, 17.62, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4]. The historical Relative Humidity data for the past 127 minutes is: [62.5, 62.58, 62.65, 62.65, 62.65, 62.65, 62.65, 62.65, 62.72, 62.8, 62.87, 62.95, 63.02, 63.02, 63.02, 63.02, 63.02, 63.02, 63.09, 63.17, 63.24, 63.32, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 63.39, 65.62, 67.85, 70.09, 72.32, 74.55, 74.64, 74.73, 74.82, 74.91, 75.0, 75.18, 75.36, 75.55, 75.73, 75.91, 76.09, 76.28, 76.46, 76.65, 76.83, 77.02, 77.21, 77.39, 77.58, 77.77, 77.86, 77.96, 78.05, 78.15, 78.24, 78.43, 78.62, 78.81, 79.0, 79.19, 78.29, 77.39, 76.5, 75.6, 74.7, 74.79, 74.88, 74.98, 75.07, 75.16, 75.34, 75.53, 75.71, 75.9]. The historical load_power data for the past 127 minutes is: [1.24, 1.24, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.21, 1.21, 1.21, 1.21]. Think about how Solar Zenith Angle, Dew Point, Relative Humidity influence load_power. Please give me a forecast for the next 37 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_79.pkl | external_data/ground_truth_data/ground_truth_data_79.pkl | external_data/context/context_79.pkl | external_data/constraint/constraint_79.pkl |
80 | electricity_prediction-max_load | I have historical Relative Humidity, Dew Point, Wind Speed data and the corresponding load_power data for the past 65 minutes. I need to ensure that the maximum allowable system load does not exceed 0.8639996457099858 MW. Think about how Relative Humidity, Dew Point, Wind Speed influence load_power. Please give me a forecast for the next 84 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Dew Point, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Dew Point, Wind Speed data and the corresponding load_power data for the past 65 minutes. I need to ensure that the maximum allowable system load does not exceed 0.8639996457099858 MW. The historical Relative Humidity data for the past 65 minutes is: [52.33, 52.25, 52.18, 52.1, 52.03, 51.97, 51.91, 51.84, 51.78, 51.72, 51.66, 51.59, 51.53, 51.46, 51.4, 51.34, 51.28, 51.21, 51.15, 51.09, 50.52, 49.94, 49.37, 48.79, 48.22, 48.16, 48.1, 48.04, 47.98, 47.92, 47.8, 47.69, 47.57, 47.46, 47.34, 47.28, 47.23, 47.17, 47.12, 47.06, 47.0, 46.94, 46.89, 46.83, 46.77, 46.7, 46.64, 46.57, 46.51, 46.44, 46.44, 46.44, 46.44, 46.44, 46.44, 46.38, 46.33, 46.27, 46.22, 46.16, 46.16, 46.16, 46.16, 46.16, 46.16]. The historical Dew Point data for the past 65 minutes is: [11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 10.84, 10.68, 10.52, 10.36, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2]. The historical Wind Speed data for the past 65 minutes is: [2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7]. The historical load_power data for the past 65 minutes is: [0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82]. Think about how Relative Humidity, Dew Point, Wind Speed influence load_power. Please give me a forecast for the next 84 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Dew Point, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_80.pkl | external_data/ground_truth_data/ground_truth_data_80.pkl | external_data/context/context_80.pkl | external_data/constraint/constraint_80.pkl |
81 | electricity_prediction-max_load | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 130 minutes. I need to ensure that the maximum allowable system load does not exceed 0.17668810827422057 MW. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 71 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 130 minutes. I need to ensure that the maximum allowable system load does not exceed 0.17668810827422057 MW. The historical Relative Humidity data for the past 130 minutes is: [89.0, 88.98, 88.98, 88.98, 88.98, 88.98, 88.98, 89.1, 89.22, 89.35, 89.47, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.82, 90.05, 90.27, 90.5, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.82, 90.91, 91.01, 91.1, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19, 91.19]. The historical Temperature data for the past 130 minutes is: [6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.58, 6.56, 6.54, 6.52, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.48, 6.46, 6.44, 6.42, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4, 6.4]. The historical wind_power data for the past 130 minutes is: [0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.18, 0.17, 0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.18, 0.17, 0.17, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 71 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_81.pkl | external_data/ground_truth_data/ground_truth_data_81.pkl | external_data/context/context_81.pkl | external_data/constraint/constraint_81.pkl |
82 | electricity_prediction-max_load | I have historical Wind Speed, Temperature, Dew Point data and the corresponding load_power data for the past 77 minutes. I need to ensure that the maximum allowable system load does not exceed 0.9668428204146625 MW. Think about how Wind Speed, Temperature, Dew Point influence load_power. Please give me a forecast for the next 62 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature, Dew Point are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Temperature, Dew Point data and the corresponding load_power data for the past 77 minutes. I need to ensure that the maximum allowable system load does not exceed 0.9668428204146625 MW. The historical Wind Speed data for the past 77 minutes is: [1.5, 1.5, 1.48, 1.46, 1.44, 1.42, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.42, 1.44, 1.46, 1.48, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]. The historical Temperature data for the past 77 minutes is: [-1.78, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8]. The historical Dew Point data for the past 77 minutes is: [-4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.48, -4.46, -4.44, -4.42, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4, -4.4]. The historical load_power data for the past 77 minutes is: [0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.81, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.82, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83]. Think about how Wind Speed, Temperature, Dew Point influence load_power. Please give me a forecast for the next 62 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature, Dew Point are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_82.pkl | external_data/ground_truth_data/ground_truth_data_82.pkl | external_data/context/context_82.pkl | external_data/constraint/constraint_82.pkl |
83 | electricity_prediction-max_load | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 122 minutes. I need to ensure that the maximum allowable system load does not exceed 0.01769179202758064 MW. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 37 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 122 minutes. I need to ensure that the maximum allowable system load does not exceed 0.01769179202758064 MW. The historical Relative Humidity data for the past 122 minutes is: [87.24, 87.36, 87.49, 87.61, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.74, 87.87, 87.99, 88.12, 88.24, 88.37, 88.37, 88.37, 88.37, 88.37, 88.37, 88.35, 88.33, 88.32, 88.3, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.28, 88.75, 89.22, 89.68, 90.15, 90.62, 90.75, 90.88, 91.01, 91.14, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.27, 91.25, 91.23, 91.22, 91.2, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.18, 91.51, 91.83, 92.16, 92.48, 92.81, 92.94, 93.07, 93.21, 93.34, 93.47, 93.47, 93.47]. The historical Wind Speed data for the past 122 minutes is: [1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]. The historical wind_power data for the past 122 minutes is: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 37 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_83.pkl | external_data/ground_truth_data/ground_truth_data_83.pkl | external_data/context/context_83.pkl | external_data/constraint/constraint_83.pkl |
84 | electricity_prediction-max_load | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 163 minutes. I need to ensure that the maximum allowable system load does not exceed 0.07627733939219794 MW. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 67 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 163 minutes. I need to ensure that the maximum allowable system load does not exceed 0.07627733939219794 MW. The historical Relative Humidity data for the past 163 minutes is: [92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.47, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.46, 92.49, 92.51, 92.54, 92.56, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.59, 92.46, 92.33, 92.21, 92.08, 91.95, 91.95, 91.95, 91.95, 91.95, 91.95, 92.12, 92.3, 92.47, 92.65, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82, 92.82]. The historical Wind Speed data for the past 163 minutes is: [3.26, 3.24, 3.22, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.18, 3.16, 3.14, 3.12, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.08, 3.06, 3.04, 3.02, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.98, 2.96, 2.94, 2.92, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9]. The historical wind_power data for the past 163 minutes is: [0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07]. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 67 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_84.pkl | external_data/ground_truth_data/ground_truth_data_84.pkl | external_data/context/context_84.pkl | external_data/constraint/constraint_84.pkl |
85 | electricity_prediction-min_load | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 78 minutes. I require that the system load is maintained above a minimum of 0.014588462112975686 MW. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 39 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 78 minutes. I require that the system load is maintained above a minimum of 0.014588462112975686 MW. The historical Temperature data for the past 78 minutes is: [25.3, 25.3, 25.3, 25.3, 25.3, 25.3, 25.28, 25.26, 25.24, 25.22, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2, 25.2]. The historical Relative Humidity data for the past 78 minutes is: [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]. The historical wind_power data for the past 78 minutes is: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 39 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_85.pkl | external_data/ground_truth_data/ground_truth_data_85.pkl | external_data/context/context_85.pkl | external_data/constraint/constraint_85.pkl |
86 | electricity_prediction-min_load | I have historical Dew Point, Solar Zenith Angle, Temperature data and the corresponding load_power data for the past 84 minutes. I require that the system load is maintained above a minimum of 1.1014744724271688 MW. Think about how Dew Point, Solar Zenith Angle, Temperature influence load_power. Please give me a forecast for the next 22 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Solar Zenith Angle, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Solar Zenith Angle, Temperature data and the corresponding load_power data for the past 84 minutes. I require that the system load is maintained above a minimum of 1.1014744724271688 MW. The historical Dew Point data for the past 84 minutes is: [8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.48, 8.46, 8.44, 8.42, 8.4, 8.4, 8.4, 8.4, 8.4, 8.4, 8.38, 8.36, 8.34, 8.32, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.28, 8.26, 8.24, 8.22, 8.2, 8.2, 8.2, 8.2, 8.2, 8.2, 8.18, 8.16, 8.14, 8.12, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.08, 8.06, 8.04, 8.02, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]. The historical Solar Zenith Angle data for the past 84 minutes is: [96.91, 97.1, 97.29, 97.47, 97.66, 97.85, 98.04, 98.23, 98.42, 98.61, 98.8, 98.99, 99.18, 99.37, 99.56, 99.75, 99.94, 100.13, 100.33, 100.52, 100.71, 100.9, 101.09, 101.28, 101.47, 101.66, 101.85, 102.04, 102.24, 102.43, 102.62, 102.81, 103.0, 103.2, 103.39, 103.58, 103.77, 103.97, 104.16, 104.36, 104.55, 104.74, 104.94, 105.13, 105.33, 105.52, 105.71, 105.91, 106.1, 106.3, 106.49, 106.68, 106.88, 107.07, 107.27, 107.46, 107.65, 107.85, 108.04, 108.24, 108.43, 108.63, 108.82, 109.02, 109.21, 109.41, 109.61, 109.8, 110.0, 110.19, 110.39, 110.59, 110.78, 110.98, 111.17, 111.37, 111.57, 111.76, 111.96, 112.15, 112.35, 112.55, 112.74, 112.94]. The historical Temperature data for the past 84 minutes is: [8.5, 8.5, 8.5, 8.5, 8.5, 8.5, 8.48, 8.46, 8.44, 8.42, 8.4, 8.4, 8.4, 8.4, 8.4, 8.4, 8.38, 8.36, 8.34, 8.32, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3, 8.28, 8.26, 8.24, 8.22, 8.2, 8.2, 8.2, 8.2, 8.2, 8.2, 8.18, 8.16, 8.14, 8.12, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.08, 8.06, 8.04, 8.02, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]. The historical load_power data for the past 84 minutes is: [1.16, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.15, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.14, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13, 1.13]. Think about how Dew Point, Solar Zenith Angle, Temperature influence load_power. Please give me a forecast for the next 22 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Solar Zenith Angle, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_86.pkl | external_data/ground_truth_data/ground_truth_data_86.pkl | external_data/context/context_86.pkl | external_data/constraint/constraint_86.pkl |
87 | electricity_prediction-min_load | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 0.029137850825324565 MW. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 70 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 0.029137850825324565 MW. The historical Temperature data for the past 143 minutes is: [8.46, 8.48, 8.5, 8.52, 8.54, 8.56, 8.58, 8.6, 8.62, 8.64, 8.66, 8.68, 8.7, 8.72, 8.74, 8.76, 8.78, 8.8, 8.82, 8.84, 8.86, 8.88, 8.9, 8.92, 8.94, 8.96, 8.98, 9.0, 9.02, 9.04, 9.06, 9.08, 9.1, 9.12, 9.14, 9.16, 9.18, 9.2, 9.22, 9.24, 9.26, 9.28, 9.3, 9.3, 9.3, 9.3, 9.3, 9.3, 9.32, 9.34, 9.36, 9.38, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.42, 9.44, 9.46, 9.48, 9.5, 9.52, 9.54, 9.56, 9.58, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.62, 9.64, 9.66, 9.68, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.72, 9.74, 9.76, 9.78, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.82, 9.84, 9.86, 9.88, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.92, 9.94, 9.96, 9.98, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]. The historical Relative Humidity data for the past 143 minutes is: [65.04, 64.95, 64.86, 64.77, 64.68, 64.6, 64.51, 64.42, 63.65, 62.88, 62.1, 61.33, 60.56, 60.48, 60.4, 60.32, 60.24, 60.16, 60.08, 60.0, 59.91, 59.83, 59.75, 59.67, 59.59, 59.51, 59.43, 59.35, 59.27, 59.19, 59.11, 59.03, 58.95, 58.87, 58.79, 58.72, 58.64, 58.56, 58.48, 58.4, 58.33, 58.25, 58.17, 58.17, 58.17, 58.17, 58.17, 58.17, 58.09, 58.01, 57.94, 57.86, 57.78, 57.78, 57.78, 57.78, 57.78, 57.78, 57.7, 57.62, 57.55, 57.47, 57.39, 57.3, 57.21, 57.13, 57.04, 56.95, 56.35, 55.76, 55.16, 54.57, 53.97, 53.9, 53.83, 53.75, 53.68, 53.61, 53.61, 53.61, 53.61, 53.61, 53.61, 53.54, 53.47, 53.39, 53.32, 53.25, 53.25, 53.25, 53.25, 53.25, 53.25, 53.18, 53.11, 53.04, 52.97, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.9, 52.43, 51.95, 51.48, 51.0, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53, 50.53]. The historical wind_power data for the past 143 minutes is: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.04, 0.04, 0.04, 0.04, 0.04]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 70 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_87.pkl | external_data/ground_truth_data/ground_truth_data_87.pkl | external_data/context/context_87.pkl | external_data/constraint/constraint_87.pkl |
88 | electricity_prediction-min_load | I have historical Solar Zenith Angle, Wind Speed, Relative Humidity data and the corresponding load_power data for the past 72 minutes. I require that the system load is maintained above a minimum of 0.9549282778373619 MW. Think about how Solar Zenith Angle, Wind Speed, Relative Humidity influence load_power. Please give me a forecast for the next 70 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Solar Zenith Angle, Wind Speed, Relative Humidity data and the corresponding load_power data for the past 72 minutes. I require that the system load is maintained above a minimum of 0.9549282778373619 MW. The historical Solar Zenith Angle data for the past 72 minutes is: [91.83, 91.66, 91.49, 91.2, 90.91, 90.61, 90.32, 90.03, 89.88, 89.73, 89.59, 89.44, 89.29, 89.13, 88.98, 88.82, 88.67, 88.51, 88.35, 88.19, 88.02, 87.86, 87.7, 87.53, 87.37, 87.2, 87.04, 86.87, 86.7, 86.53, 86.35, 86.18, 86.01, 85.84, 85.67, 85.49, 85.32, 85.15, 84.98, 84.8, 84.63, 84.45, 84.28, 84.1, 83.93, 83.75, 83.58, 83.4, 83.22, 83.04, 82.87, 82.69, 82.51, 82.33, 82.15, 81.98, 81.8, 81.62, 81.44, 81.26, 81.09, 80.91, 80.73, 80.55, 80.37, 80.19, 80.01, 79.83, 79.65, 79.47, 79.29, 79.11]. The historical Wind Speed data for the past 72 minutes is: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]. The historical Relative Humidity data for the past 72 minutes is: [87.41, 87.28, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.15, 87.02, 86.89, 86.77, 86.64, 86.51, 85.76, 85.02, 84.27, 83.53, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.78, 82.66, 82.54, 82.41, 82.29, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.17, 82.05, 81.93, 81.8, 81.68, 81.56, 81.44, 81.32, 81.2, 81.08, 80.96, 80.96, 80.96, 80.96, 80.96]. The historical load_power data for the past 72 minutes is: [0.92, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.01, 1.01]. Think about how Solar Zenith Angle, Wind Speed, Relative Humidity influence load_power. Please give me a forecast for the next 70 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Solar Zenith Angle, Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_88.pkl | external_data/ground_truth_data/ground_truth_data_88.pkl | external_data/context/context_88.pkl | external_data/constraint/constraint_88.pkl |
89 | electricity_prediction-min_load | I have historical Dew Point, GHI, Temperature, Solar Zenith Angle, DHI data and the corresponding solar_power data for the past 122 minutes. I require that the system load is maintained above a minimum of 0.006635489147421651 MW. Think about how Dew Point, GHI, Temperature, Solar Zenith Angle, DHI influence solar_power. Please give me a forecast for the next 57 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, GHI, Temperature, Solar Zenith Angle, DHI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, GHI, Temperature, Solar Zenith Angle, DHI data and the corresponding solar_power data for the past 122 minutes. I require that the system load is maintained above a minimum of 0.006635489147421651 MW. The historical Dew Point data for the past 122 minutes is: [21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.02, 21.04, 21.06, 21.08, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.12, 21.14, 21.16, 21.18, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.22, 21.24, 21.26, 21.28, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.32, 21.34, 21.36, 21.38, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.42, 21.44, 21.46, 21.48, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.5, 21.58, 21.66, 21.74, 21.82, 21.9, 21.92, 21.94, 21.96, 21.98, 22.0]. The historical GHI data for the past 122 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 5.4, 5.8, 6.2, 6.6, 7.0, 7.2, 7.4, 7.6, 7.8, 8.0, 8.8, 9.6, 10.4, 11.2, 12.0, 12.6, 13.2, 13.8, 14.4, 15.0, 15.6, 16.2, 16.8, 17.4, 18.0, 18.6, 19.2, 19.8, 20.4, 21.0, 21.6, 22.2, 22.8, 23.4, 24.0, 24.8, 25.6, 26.4, 27.2, 28.0, 28.8, 29.6, 30.4, 31.2, 32.0, 29.4, 26.8, 24.2, 21.6, 19.0]. The historical Temperature data for the past 122 minutes is: [21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.02, 21.04, 21.06, 21.08, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.12, 21.14, 21.16, 21.18, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.22, 21.24, 21.26, 21.28, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.32, 21.34, 21.36, 21.38, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.42, 21.44, 21.46, 21.48, 21.5, 21.52, 21.54, 21.56, 21.58, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.62, 21.64, 21.66, 21.68, 21.7, 21.72, 21.74, 21.76, 21.78, 21.8, 21.82, 21.84, 21.86, 21.88, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.92, 21.94, 21.96, 21.98, 22.0]. The historical Solar Zenith Angle data for the past 122 minutes is: [99.16, 99.02, 98.87, 98.73, 98.58, 98.44, 98.29, 98.14, 98.0, 97.85, 97.71, 97.56, 97.41, 97.26, 97.11, 96.96, 96.81, 96.66, 96.51, 96.35, 96.2, 96.05, 95.9, 95.75, 95.59, 95.44, 95.29, 95.14, 94.98, 94.83, 94.67, 94.52, 94.36, 94.21, 94.05, 93.9, 93.74, 93.58, 93.42, 93.27, 93.11, 92.95, 92.79, 92.63, 92.47, 92.31, 92.15, 91.99, 91.83, 91.67, 91.51, 91.35, 91.08, 90.81, 90.53, 90.26, 89.99, 89.85, 89.71, 89.57, 89.43, 89.29, 89.14, 89.0, 88.85, 88.71, 88.56, 88.41, 88.25, 88.1, 87.94, 87.79, 87.63, 87.47, 87.32, 87.16, 87.0, 86.84, 86.68, 86.51, 86.35, 86.19, 86.02, 85.86, 85.69, 85.53, 85.36, 85.19, 85.03, 84.86, 84.7, 84.53, 84.36, 84.19, 84.02, 83.85, 83.68, 83.51, 83.34, 83.17, 83.0, 82.83, 82.66, 82.48, 82.31, 82.13, 81.96, 81.79, 81.62, 81.44, 81.27, 81.1, 80.92, 80.75, 80.57, 80.4, 80.22, 80.04, 79.87, 79.69, 79.52, 79.34]. The historical DHI data for the past 122 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 5.4, 5.8, 6.2, 6.6, 7.0, 7.2, 7.4, 7.6, 7.8, 8.0, 8.8, 9.6, 10.4, 11.2, 12.0, 12.6, 13.2, 13.8, 14.4, 15.0, 15.6, 16.2, 16.8, 17.4, 18.0, 18.6, 19.2, 19.8, 20.4, 21.0, 21.6, 22.2, 22.8, 23.4, 24.0, 24.8, 25.6, 26.4, 27.2, 28.0, 28.8, 29.6, 30.4, 31.2, 32.0, 29.4, 26.8, 24.2, 21.6, 19.0]. The historical solar_power data for the past 122 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01]. Think about how Dew Point, GHI, Temperature, Solar Zenith Angle, DHI influence solar_power. Please give me a forecast for the next 57 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, GHI, Temperature, Solar Zenith Angle, DHI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_89.pkl | external_data/ground_truth_data/ground_truth_data_89.pkl | external_data/context/context_89.pkl | external_data/constraint/constraint_89.pkl |
90 | electricity_prediction-min_load | I have historical Dew Point, Solar Zenith Angle, Wind Speed data and the corresponding load_power data for the past 154 minutes. I require that the system load is maintained above a minimum of 1.391069200224733 MW. Think about how Dew Point, Solar Zenith Angle, Wind Speed influence load_power. Please give me a forecast for the next 13 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Solar Zenith Angle, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Solar Zenith Angle, Wind Speed data and the corresponding load_power data for the past 154 minutes. I require that the system load is maintained above a minimum of 1.391069200224733 MW. The historical Dew Point data for the past 154 minutes is: [18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.12, 18.04, 17.96, 17.88, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.74, 17.68, 17.62, 17.56, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5]. The historical Solar Zenith Angle data for the past 154 minutes is: [38.71, 38.5, 38.28, 38.07, 37.86, 37.65, 37.44, 37.22, 37.01, 36.8, 36.59, 36.38, 36.17, 35.96, 35.75, 35.54, 35.33, 35.11, 34.9, 34.69, 34.48, 34.27, 34.07, 33.86, 33.65, 33.44, 33.23, 33.02, 32.81, 32.6, 32.39, 32.19, 31.98, 31.78, 31.57, 31.36, 31.15, 30.95, 30.74, 30.53, 30.33, 30.12, 29.92, 29.71, 29.51, 29.31, 29.1, 28.9, 28.69, 28.49, 28.29, 28.09, 27.88, 27.68, 27.48, 27.28, 27.08, 26.88, 26.68, 26.48, 26.28, 26.09, 25.89, 25.7, 25.5, 25.3, 25.11, 24.91, 24.72, 24.52, 24.33, 24.14, 23.94, 23.75, 23.56, 23.37, 23.18, 22.99, 22.8, 22.61, 22.43, 22.24, 22.06, 21.87, 21.69, 21.51, 21.33, 21.14, 20.96, 20.78, 20.6, 20.42, 20.25, 20.07, 19.89, 19.72, 19.55, 19.37, 19.2, 19.03, 18.87, 18.7, 18.54, 18.37, 18.21, 18.05, 17.89, 17.73, 17.57, 17.41, 17.26, 17.11, 16.96, 16.81, 16.66, 16.52, 16.38, 16.23, 16.09, 15.95, 15.82, 15.69, 15.56, 15.43, 15.3, 15.18, 15.06, 14.94, 14.82, 14.7, 14.59, 14.49, 14.38, 14.28, 14.17, 14.08, 13.99, 13.89, 13.8, 13.71, 13.64, 13.56, 13.49, 13.41, 13.34, 13.28, 13.23, 13.17, 13.12, 13.06, 13.02, 12.98, 12.94, 12.9]. The historical Wind Speed data for the past 154 minutes is: [2.1, 2.1, 2.1, 2.1, 2.1, 2.12, 2.14, 2.16, 2.18, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.22, 2.24, 2.26, 2.28, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.32, 2.34, 2.36, 2.38, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.42, 2.44, 2.46, 2.48, 2.5, 2.5, 2.5, 2.5, 2.5]. The historical load_power data for the past 154 minutes is: [1.14, 1.15, 1.15, 1.15, 1.15, 1.16, 1.16, 1.16, 1.16, 1.16, 1.17, 1.17, 1.17, 1.17, 1.18, 1.18, 1.18, 1.18, 1.19, 1.19, 1.19, 1.19, 1.19, 1.2, 1.2, 1.2, 1.2, 1.21, 1.21, 1.21, 1.21, 1.21, 1.22, 1.22, 1.22, 1.22, 1.23, 1.23, 1.23, 1.23, 1.24, 1.24, 1.24, 1.24, 1.24, 1.25, 1.25, 1.25, 1.25, 1.26, 1.26, 1.26, 1.26, 1.26, 1.27, 1.27, 1.27, 1.27, 1.28, 1.28, 1.28, 1.28, 1.29, 1.29, 1.29, 1.29, 1.29, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.31, 1.31, 1.31, 1.31, 1.31, 1.31, 1.32, 1.32, 1.32, 1.32, 1.32, 1.32, 1.33, 1.33, 1.33, 1.33, 1.33, 1.33, 1.34, 1.34, 1.34, 1.34, 1.34, 1.34, 1.35, 1.35, 1.35, 1.35, 1.35, 1.36, 1.36, 1.36, 1.36, 1.36, 1.36, 1.37, 1.37, 1.37, 1.37, 1.37, 1.37, 1.38, 1.38, 1.38, 1.38, 1.38, 1.38, 1.39, 1.39, 1.39, 1.39, 1.39, 1.39, 1.39, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 1.41, 1.42, 1.42, 1.42, 1.42, 1.42, 1.42]. Think about how Dew Point, Solar Zenith Angle, Wind Speed influence load_power. Please give me a forecast for the next 13 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Solar Zenith Angle, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_90.pkl | external_data/ground_truth_data/ground_truth_data_90.pkl | external_data/context/context_90.pkl | external_data/constraint/constraint_90.pkl |
91 | electricity_prediction-min_load | I have historical Temperature, Dew Point, Relative Humidity data and the corresponding load_power data for the past 146 minutes. I require that the system load is maintained above a minimum of 0.9360556012244476 MW. Think about how Temperature, Dew Point, Relative Humidity influence load_power. Please give me a forecast for the next 20 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Dew Point, Relative Humidity data and the corresponding load_power data for the past 146 minutes. I require that the system load is maintained above a minimum of 0.9360556012244476 MW. The historical Temperature data for the past 146 minutes is: [12.9, 12.88, 12.86, 12.84, 12.82, 12.8, 12.78, 12.76, 12.74, 12.72, 12.7, 12.68, 12.66, 12.64, 12.62, 12.6, 12.58, 12.56, 12.54, 12.52, 12.5, 12.48, 12.46, 12.44, 12.42, 12.4, 12.38, 12.36, 12.34, 12.32, 12.3, 12.28, 12.26, 12.24, 12.22, 12.2, 12.18, 12.16, 12.14, 12.12, 12.1, 12.08, 12.06, 12.04, 12.02, 12.0, 11.98, 11.96, 11.94, 11.92, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, 11.88, 11.86, 11.84, 11.82, 11.8, 11.78, 11.76, 11.74, 11.72, 11.7, 11.7, 11.7, 11.7, 11.7, 11.7, 11.68, 11.66, 11.64, 11.62, 11.6, 11.58, 11.56, 11.54, 11.52, 11.5, 11.48, 11.46, 11.44, 11.42, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.38, 11.36, 11.34, 11.32, 11.3, 11.28, 11.26, 11.24, 11.22, 11.2, 11.18, 11.16, 11.14, 11.12, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1]. The historical Dew Point data for the past 146 minutes is: [10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.38, 10.36, 10.34, 10.32, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.22, 10.14, 10.06, 9.98, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9]. The historical Relative Humidity data for the past 146 minutes is: [84.51, 84.62, 84.73, 84.84, 84.95, 85.06, 85.17, 85.28, 85.4, 85.51, 85.62, 85.73, 85.84, 85.96, 86.07, 86.18, 86.18, 86.18, 86.19, 86.19, 86.19, 86.3, 86.41, 86.53, 86.64, 86.75, 86.86, 86.98, 87.09, 87.21, 87.32, 87.44, 87.55, 87.67, 87.78, 87.9, 88.01, 88.13, 88.24, 88.36, 88.47, 88.59, 88.71, 88.82, 88.94, 89.06, 89.18, 89.29, 89.41, 89.52, 89.64, 89.64, 89.64, 89.64, 89.64, 89.64, 89.76, 89.88, 90.0, 90.12, 90.24, 90.36, 90.48, 90.59, 90.71, 90.83, 90.83, 90.83, 90.83, 90.83, 90.83, 90.95, 91.07, 91.19, 91.31, 91.43, 91.16, 90.89, 90.61, 90.34, 90.07, 90.19, 90.31, 90.42, 90.54, 90.66, 90.66, 90.66, 90.66, 90.66, 90.66, 90.78, 90.9, 91.03, 91.15, 91.27, 91.39, 91.51, 91.63, 91.75, 91.87, 91.99, 92.11, 92.24, 92.36, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48, 92.48]. The historical load_power data for the past 146 minutes is: [0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.98, 0.98, 0.98, 0.98, 0.98, 0.98]. Think about how Temperature, Dew Point, Relative Humidity influence load_power. Please give me a forecast for the next 20 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Dew Point, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_91.pkl | external_data/ground_truth_data/ground_truth_data_91.pkl | external_data/context/context_91.pkl | external_data/constraint/constraint_91.pkl |
92 | electricity_prediction-min_load | I have historical Dew Point, Wind Speed, Temperature data and the corresponding load_power data for the past 121 minutes. I require that the system load is maintained above a minimum of 0.6742048000987942 MW. Think about how Dew Point, Wind Speed, Temperature influence load_power. Please give me a forecast for the next 20 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Wind Speed, Temperature data and the corresponding load_power data for the past 121 minutes. I require that the system load is maintained above a minimum of 0.6742048000987942 MW. The historical Dew Point data for the past 121 minutes is: [12.8, 12.78, 12.76, 12.74, 12.72, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.68, 12.66, 12.64, 12.62, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6, 12.6]. The historical Wind Speed data for the past 121 minutes is: [0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.58, 0.56, 0.54, 0.52, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]. The historical Temperature data for the past 121 minutes is: [13.5, 13.48, 13.46, 13.44, 13.42, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.38, 13.36, 13.34, 13.32, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.28, 13.26, 13.24, 13.22, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.18, 13.16, 13.14, 13.12, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1]. The historical load_power data for the past 121 minutes is: [0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.75]. Think about how Dew Point, Wind Speed, Temperature influence load_power. Please give me a forecast for the next 20 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_92.pkl | external_data/ground_truth_data/ground_truth_data_92.pkl | external_data/context/context_92.pkl | external_data/constraint/constraint_92.pkl |
93 | electricity_prediction-min_load | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 172 minutes. I require that the system load is maintained above a minimum of 0.0005904985641690923 MW. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 50 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 172 minutes. I require that the system load is maintained above a minimum of 0.0005904985641690923 MW. The historical Wind Speed data for the past 172 minutes is: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]. The historical Relative Humidity data for the past 172 minutes is: [85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.53, 85.77, 86.01, 86.26, 86.5, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.87, 86.99, 87.12, 87.24, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.37, 87.5, 87.63, 87.75, 87.88, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.01, 88.03, 88.06, 88.08, 88.11, 88.13, 88.26, 88.39, 88.52, 88.65, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.78, 88.91, 89.04, 89.17, 89.3, 89.43, 89.41, 89.39, 89.37, 89.35, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.33, 89.46, 89.59, 89.73, 89.86, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.99, 89.87, 89.76, 89.64, 89.53, 89.41, 89.54, 89.67, 89.8, 89.93, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.06, 90.19, 90.32, 90.46, 90.59, 90.72, 90.72, 90.72]. The historical wind_power data for the past 172 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 50 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_93.pkl | external_data/ground_truth_data/ground_truth_data_93.pkl | external_data/context/context_93.pkl | external_data/constraint/constraint_93.pkl |
94 | electricity_prediction-min_load | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 171 minutes. I require that the system load is maintained above a minimum of 0.06823206046509234 MW. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 38 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 171 minutes. I require that the system load is maintained above a minimum of 0.06823206046509234 MW. The historical Relative Humidity data for the past 171 minutes is: [98.62, 98.62, 98.62, 98.62, 98.62, 98.62, 98.62, 98.62, 98.37, 98.11, 97.86, 97.6, 97.35, 97.35, 97.35, 97.35, 97.35, 97.35, 97.47, 97.59, 97.7, 97.82, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 97.94, 98.06, 98.18, 98.29, 98.41, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.53, 98.65, 98.77, 98.88, 99.0, 99.12, 98.74, 98.36, 97.99, 97.61, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.23, 97.35, 97.46, 97.58, 97.69, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.81, 97.93, 98.05, 98.17, 98.29, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.41, 98.05, 97.69, 97.32, 96.96, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.6, 96.72, 96.83, 96.95, 97.06, 97.18, 97.18, 97.18, 97.18, 97.18, 97.18, 97.06, 96.95, 96.83, 96.72, 96.6, 96.48, 96.37, 96.25, 96.14, 96.02, 96.02, 96.02, 96.02]. The historical Temperature data for the past 171 minutes is: [23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.3, 23.28, 23.26, 23.24, 23.22, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.18, 23.16, 23.14, 23.12, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.1, 23.08, 23.06, 23.04, 23.02, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 22.98, 22.96, 22.94, 22.92, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.88, 22.86, 22.84, 22.82, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.78, 22.76, 22.74, 22.72, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.68, 22.66, 22.64, 22.62, 22.6, 22.6, 22.6, 22.6, 22.6, 22.6, 22.62, 22.64, 22.66, 22.68, 22.7, 22.72, 22.74, 22.76, 22.78, 22.8, 22.8, 22.8, 22.8]. The historical wind_power data for the past 171 minutes is: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 38 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_94.pkl | external_data/ground_truth_data/ground_truth_data_94.pkl | external_data/context/context_94.pkl | external_data/constraint/constraint_94.pkl |
95 | electricity_prediction-min_load | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 140 minutes. I require that the system load is maintained above a minimum of 0.0007564242760928808 MW. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 89 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 140 minutes. I require that the system load is maintained above a minimum of 0.0007564242760928808 MW. The historical Wind Speed data for the past 140 minutes is: [0.7, 0.68, 0.66, 0.64, 0.62, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.58, 0.56, 0.54, 0.52, 0.5, 0.5, 0.5, 0.5, 0.5]. The historical Temperature data for the past 140 minutes is: [18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.18, 18.16, 18.14, 18.12, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.08, 18.06, 18.04, 18.02, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 17.98, 17.96, 17.94, 17.92, 17.9, 17.92, 17.94, 17.96, 17.98, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.02, 18.04, 18.06, 18.08, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.12, 18.14, 18.16, 18.18, 18.2, 18.2, 18.2, 18.2, 18.2]. The historical wind_power data for the past 140 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 89 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_95.pkl | external_data/ground_truth_data/ground_truth_data_95.pkl | external_data/context/context_95.pkl | external_data/constraint/constraint_95.pkl |
96 | electricity_prediction-min_load | I have historical Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle data and the corresponding solar_power data for the past 129 minutes. I require that the system load is maintained above a minimum of 0.6496606038526249 MW. Think about how Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 66 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle data and the corresponding solar_power data for the past 129 minutes. I require that the system load is maintained above a minimum of 0.6496606038526249 MW. The historical Dew Point data for the past 129 minutes is: [-4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -4.0, -3.84, -3.68, -3.52, -3.36, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.2, -3.14, -3.08, -3.02, -2.96, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9, -2.9]. The historical DNI data for the past 129 minutes is: [805.0, 807.0, 809.0, 810.8, 812.6, 814.4, 816.2, 818.0, 819.6, 821.2, 822.8, 824.4, 826.0, 827.8, 829.6, 831.4, 833.2, 835.0, 836.6, 838.2, 839.8, 841.4, 843.0, 844.4, 845.8, 847.2, 848.6, 850.0, 851.4, 852.8, 854.2, 855.6, 857.0, 858.2, 859.4, 860.6, 861.8, 863.0, 864.4, 865.8, 867.2, 868.6, 870.0, 871.0, 872.0, 873.0, 874.0, 875.0, 876.2, 877.4, 878.6, 879.8, 881.0, 882.0, 883.0, 884.0, 885.0, 886.0, 887.0, 888.0, 889.0, 890.0, 891.0, 892.0, 893.0, 894.0, 895.0, 896.0, 896.8, 897.6, 898.4, 899.2, 900.0, 899.2, 898.4, 897.6, 896.8, 896.0, 896.8, 897.6, 898.4, 899.2, 900.0, 900.2, 900.4, 900.6, 900.8, 901.0, 901.2, 901.4, 901.6, 901.8, 902.0, 902.6, 903.2, 903.8, 904.4, 905.0, 905.8, 906.6, 907.4, 908.2, 909.0, 909.6, 910.2, 910.8, 911.4, 912.0, 912.6, 913.2, 913.8, 914.4, 915.0, 902.6, 890.2, 877.8, 865.4, 853.0, 866.4, 879.8, 893.2, 906.6, 920.0, 917.0, 914.0, 911.0, 908.0, 905.0, 909.0]. The historical GHI data for the past 129 minutes is: [407.6, 410.8, 414.0, 417.2, 420.4, 423.6, 426.8, 430.0, 433.0, 436.0, 439.0, 442.0, 445.0, 448.2, 451.4, 454.6, 457.8, 461.0, 464.0, 467.0, 470.0, 473.0, 476.0, 479.0, 482.0, 485.0, 488.0, 491.0, 493.8, 496.6, 499.4, 502.2, 505.0, 508.0, 511.0, 514.0, 517.0, 520.0, 522.8, 525.6, 528.4, 531.2, 534.0, 536.8, 539.6, 542.4, 545.2, 548.0, 550.6, 553.2, 555.8, 558.4, 561.0, 563.6, 566.2, 568.8, 571.4, 574.0, 576.6, 579.2, 581.8, 584.4, 587.0, 589.6, 592.2, 594.8, 597.4, 600.0, 602.4, 604.8, 607.2, 609.6, 612.0, 614.4, 616.8, 619.2, 621.6, 624.0, 626.2, 628.4, 630.6, 632.8, 635.0, 629.4, 623.8, 618.2, 612.6, 607.0, 616.4, 625.8, 635.2, 644.6, 654.0, 656.0, 658.0, 660.0, 662.0, 664.0, 666.2, 668.4, 670.6, 672.8, 675.0, 676.8, 678.6, 680.4, 682.2, 684.0, 686.0, 688.0, 690.0, 692.0, 694.0, 682.8, 671.6, 660.4, 649.2, 638.0, 652.8, 667.6, 682.4, 697.2, 712.0, 704.4, 696.8, 689.2, 681.6, 674.0, 684.8]. The historical Relative Humidity data for the past 129 minutes is: [63.5, 63.32, 63.14, 62.87, 62.61, 62.34, 62.08, 61.81, 61.64, 61.46, 61.29, 61.11, 60.94, 61.43, 61.92, 62.42, 62.91, 63.4, 63.22, 63.05, 62.87, 62.7, 62.52, 62.26, 62.0, 61.73, 61.47, 61.21, 61.03, 60.84, 60.66, 60.47, 60.29, 60.12, 59.95, 59.79, 59.62, 59.45, 59.2, 58.96, 58.71, 58.47, 58.22, 58.14, 58.06, 57.97, 57.89, 57.81, 57.65, 57.49, 57.33, 57.17, 57.01, 56.93, 56.85, 56.78, 56.7, 56.62, 56.54, 56.46, 56.38, 56.3, 56.22, 56.07, 55.91, 55.76, 55.6, 55.45, 55.37, 55.29, 55.22, 55.14, 55.06, 55.13, 55.2, 55.26, 55.33, 55.4, 55.32, 55.25, 55.17, 55.1, 55.02, 54.94, 54.87, 54.79, 54.72, 54.64, 54.49, 54.34, 54.19, 54.04, 53.89, 53.82, 53.74, 53.67, 53.59, 53.52, 53.37, 53.22, 53.08, 52.93, 52.78, 52.71, 52.64, 52.56, 52.49, 52.42, 52.35, 52.28, 52.2, 52.13, 52.06, 52.06, 52.06, 52.06, 52.06, 52.06, 51.99, 51.92, 51.84, 51.77, 51.7, 51.63, 51.56, 51.49, 51.42, 51.35, 51.28]. The historical Solar Zenith Angle data for the past 129 minutes is: [66.56, 66.4, 66.24, 66.08, 65.92, 65.76, 65.6, 65.44, 65.28, 65.12, 64.97, 64.81, 64.65, 64.49, 64.34, 64.18, 64.03, 63.87, 63.72, 63.56, 63.41, 63.25, 63.1, 62.95, 62.8, 62.64, 62.49, 62.34, 62.19, 62.04, 61.88, 61.73, 61.58, 61.43, 61.28, 61.14, 60.99, 60.84, 60.69, 60.54, 60.4, 60.25, 60.1, 59.96, 59.81, 59.67, 59.52, 59.38, 59.24, 59.09, 58.95, 58.8, 58.66, 58.52, 58.38, 58.24, 58.1, 57.96, 57.82, 57.68, 57.55, 57.41, 57.27, 57.13, 57.0, 56.86, 56.73, 56.59, 56.46, 56.32, 56.19, 56.05, 55.92, 55.79, 55.66, 55.53, 55.4, 55.27, 55.14, 55.01, 54.89, 54.76, 54.63, 54.51, 54.38, 54.26, 54.13, 54.01, 53.89, 53.77, 53.64, 53.52, 53.4, 53.28, 53.16, 53.04, 52.92, 52.8, 52.69, 52.57, 52.46, 52.34, 52.23, 52.12, 52.01, 51.89, 51.78, 51.67, 51.56, 51.45, 51.34, 51.23, 51.12, 51.02, 50.91, 50.81, 50.7, 50.6, 50.5, 50.4, 50.29, 50.19, 50.09, 49.99, 49.89, 49.8, 49.7, 49.6, 49.51]. The historical solar_power data for the past 129 minutes is: [0.43, 0.44, 0.44, 0.44, 0.45, 0.45, 0.45, 0.46, 0.46, 0.46, 0.47, 0.47, 0.47, 0.48, 0.48, 0.48, 0.49, 0.49, 0.49, 0.5, 0.5, 0.5, 0.51, 0.51, 0.51, 0.51, 0.52, 0.52, 0.52, 0.53, 0.53, 0.53, 0.54, 0.54, 0.54, 0.54, 0.55, 0.55, 0.55, 0.56, 0.56, 0.56, 0.56, 0.57, 0.57, 0.57, 0.57, 0.58, 0.58, 0.58, 0.58, 0.59, 0.59, 0.59, 0.6, 0.6, 0.6, 0.6, 0.61, 0.61, 0.61, 0.61, 0.62, 0.62, 0.62, 0.62, 0.62, 0.63, 0.63, 0.63, 0.63, 0.63, 0.64, 0.64, 0.64, 0.64, 0.64, 0.65, 0.65, 0.65, 0.65, 0.65, 0.66, 0.65, 0.64, 0.64, 0.63, 0.63, 0.64, 0.65, 0.65, 0.66, 0.67, 0.67, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.69, 0.68, 0.67, 0.66, 0.65, 0.66, 0.68, 0.69, 0.71, 0.72, 0.71, 0.71, 0.7, 0.69, 0.68, 0.69]. Think about how Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 66 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, DNI, GHI, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_96.pkl | external_data/ground_truth_data/ground_truth_data_96.pkl | external_data/context/context_96.pkl | external_data/constraint/constraint_96.pkl |
97 | electricity_prediction-min_load | I have historical Dew Point, Relative Humidity, Temperature, DNI, DHI data and the corresponding solar_power data for the past 158 minutes. I require that the system load is maintained above a minimum of 0.5804184627686717 MW. Think about how Dew Point, Relative Humidity, Temperature, DNI, DHI influence solar_power. Please give me a forecast for the next 25 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Temperature, DNI, DHI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Relative Humidity, Temperature, DNI, DHI data and the corresponding solar_power data for the past 158 minutes. I require that the system load is maintained above a minimum of 0.5804184627686717 MW. The historical Dew Point data for the past 158 minutes is: [3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 4.04, 4.18, 4.32, 4.46, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.56, 4.52, 4.48, 4.44, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.14, 3.88, 3.62, 3.36, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1]. The historical Relative Humidity data for the past 158 minutes is: [68.41, 68.23, 67.96, 67.69, 67.41, 67.14, 66.87, 66.61, 66.34, 66.08, 65.81, 65.55, 65.38, 65.2, 65.03, 64.85, 64.68, 65.01, 65.34, 65.68, 66.01, 66.34, 66.08, 65.82, 65.55, 65.29, 65.03, 64.86, 64.69, 64.51, 64.34, 64.17, 63.92, 63.67, 63.41, 63.16, 62.91, 62.66, 62.42, 62.17, 61.93, 61.68, 61.52, 61.36, 61.2, 61.04, 60.88, 60.56, 60.25, 59.93, 59.62, 59.3, 59.06, 58.81, 58.57, 58.32, 58.08, 57.86, 57.63, 57.41, 57.18, 56.96, 56.67, 56.37, 56.08, 55.78, 55.49, 55.28, 55.06, 54.85, 54.63, 54.42, 54.21, 54.0, 53.79, 53.58, 53.37, 52.99, 52.62, 52.24, 51.87, 51.49, 51.29, 51.09, 50.9, 50.7, 50.5, 50.31, 50.12, 49.92, 49.73, 49.54, 49.29, 49.04, 48.78, 48.53, 48.28, 48.1, 47.92, 47.73, 47.55, 47.37, 47.13, 46.89, 46.66, 46.42, 46.18, 46.06, 45.94, 45.83, 45.71, 45.59, 45.42, 45.25, 45.07, 44.9, 44.73, 44.56, 44.39, 44.23, 44.06, 43.89, 43.73, 43.56, 43.4, 43.23, 43.07, 42.91, 42.75, 42.58, 42.42, 42.26, 42.15, 42.05, 41.94, 41.84, 41.73, 40.83, 39.94, 39.04, 38.15, 37.25, 37.1, 36.96, 36.81, 36.67, 36.52, 36.38, 36.25, 36.11, 35.98, 35.84, 35.71, 35.58, 35.44, 35.31, 35.18, 35.09]. The historical Temperature data for the past 158 minutes is: [9.46, 9.5, 9.56, 9.62, 9.68, 9.74, 9.8, 9.86, 9.92, 9.98, 10.04, 10.1, 10.14, 10.18, 10.22, 10.26, 10.3, 10.36, 10.42, 10.48, 10.54, 10.6, 10.66, 10.72, 10.78, 10.84, 10.9, 10.94, 10.98, 11.02, 11.06, 11.1, 11.16, 11.22, 11.28, 11.34, 11.4, 11.46, 11.52, 11.58, 11.64, 11.7, 11.74, 11.78, 11.82, 11.86, 11.9, 11.98, 12.06, 12.14, 12.22, 12.3, 12.36, 12.42, 12.48, 12.54, 12.6, 12.66, 12.72, 12.78, 12.84, 12.9, 12.98, 13.06, 13.14, 13.22, 13.3, 13.36, 13.42, 13.48, 13.54, 13.6, 13.66, 13.72, 13.78, 13.84, 13.9, 13.98, 14.06, 14.14, 14.22, 14.3, 14.36, 14.42, 14.48, 14.54, 14.6, 14.66, 14.72, 14.78, 14.84, 14.9, 14.98, 15.06, 15.14, 15.22, 15.3, 15.36, 15.42, 15.48, 15.54, 15.6, 15.68, 15.76, 15.84, 15.92, 16.0, 16.04, 16.08, 16.12, 16.16, 16.2, 16.26, 16.32, 16.38, 16.44, 16.5, 16.56, 16.62, 16.68, 16.74, 16.8, 16.86, 16.92, 16.98, 17.04, 17.1, 17.16, 17.22, 17.28, 17.34, 17.4, 17.44, 17.48, 17.52, 17.56, 17.6, 17.66, 17.72, 17.78, 17.84, 17.9, 17.96, 18.02, 18.08, 18.14, 18.2, 18.26, 18.32, 18.38, 18.44, 18.5, 18.56, 18.62, 18.68, 18.74, 18.8, 18.84]. The historical DNI data for the past 158 minutes is: [166.0, 169.0, 270.6, 372.2, 473.8, 575.4, 677.0, 681.0, 685.0, 689.0, 693.0, 697.0, 700.6, 704.2, 707.8, 711.4, 715.0, 717.8, 720.6, 723.4, 726.2, 729.0, 732.2, 735.4, 738.6, 741.8, 745.0, 747.8, 750.6, 753.4, 756.2, 759.0, 761.8, 764.6, 767.4, 770.2, 773.0, 775.4, 777.8, 780.2, 782.6, 785.0, 787.4, 789.8, 792.2, 794.6, 797.0, 799.2, 801.4, 803.6, 805.8, 808.0, 810.0, 812.0, 814.0, 816.0, 818.0, 820.0, 822.0, 824.0, 826.0, 828.0, 829.8, 831.6, 833.4, 835.2, 837.0, 838.6, 840.2, 841.8, 843.4, 845.0, 846.6, 848.2, 849.8, 851.4, 853.0, 854.4, 855.8, 857.2, 858.6, 860.0, 861.4, 862.8, 864.2, 865.6, 867.0, 868.4, 869.8, 871.2, 872.6, 874.0, 875.2, 876.4, 877.6, 878.8, 880.0, 881.2, 882.4, 883.6, 884.8, 886.0, 887.2, 888.4, 889.6, 890.8, 892.0, 893.0, 894.0, 895.0, 896.0, 897.0, 898.0, 899.0, 900.0, 901.0, 902.0, 903.0, 904.0, 905.0, 906.0, 907.0, 907.8, 908.6, 909.4, 910.2, 911.0, 911.8, 912.6, 913.4, 914.2, 915.0, 915.8, 916.6, 917.4, 918.2, 919.0, 919.8, 920.6, 921.4, 922.2, 923.0, 923.8, 924.6, 925.4, 926.2, 927.0, 927.6, 928.2, 928.8, 929.4, 930.0, 930.6, 931.2, 931.8, 932.4, 933.0, 933.6]. The historical DHI data for the past 158 minutes is: [70.8, 72.0, 66.0, 60.0, 54.0, 48.0, 42.0, 42.2, 42.4, 42.6, 42.8, 43.0, 43.4, 43.8, 44.2, 44.6, 45.0, 45.4, 45.8, 46.2, 46.6, 47.0, 47.2, 47.4, 47.6, 47.8, 48.0, 48.2, 48.4, 48.6, 48.8, 49.0, 49.4, 49.8, 50.2, 50.6, 51.0, 51.2, 51.4, 51.6, 51.8, 52.0, 52.2, 52.4, 52.6, 52.8, 53.0, 53.2, 53.4, 53.6, 53.8, 54.0, 54.2, 54.4, 54.6, 54.8, 55.0, 55.2, 55.4, 55.6, 55.8, 56.0, 56.0, 56.0, 56.0, 56.0, 56.0, 56.2, 56.4, 56.6, 56.8, 57.0, 57.2, 57.4, 57.6, 57.8, 58.0, 58.2, 58.4, 58.6, 58.8, 59.0, 59.2, 59.4, 59.6, 59.8, 60.0, 60.2, 60.4, 60.6, 60.8, 61.0, 61.0, 61.0, 61.0, 61.0, 61.0, 61.2, 61.4, 61.6, 61.8, 62.0, 62.0, 62.0, 62.0, 62.0, 62.0, 62.2, 62.4, 62.6, 62.8, 63.0, 63.2, 63.4, 63.6, 63.8, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.2, 64.4, 64.6, 64.8, 65.0, 65.0, 65.0, 65.0, 65.0, 65.0, 65.2, 65.4, 65.6, 65.8, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.0, 66.2, 66.4, 66.6, 66.8, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0, 67.0]. The historical solar_power data for the past 158 minutes is: [0.11, 0.12, 0.14, 0.17, 0.2, 0.23, 0.25, 0.26, 0.26, 0.26, 0.27, 0.27, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.31, 0.31, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.34, 0.34, 0.34, 0.35, 0.35, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.41, 0.41, 0.41, 0.42, 0.42, 0.42, 0.42, 0.43, 0.43, 0.43, 0.44, 0.44, 0.44, 0.44, 0.45, 0.45, 0.45, 0.46, 0.46, 0.46, 0.46, 0.47, 0.47, 0.47, 0.48, 0.48, 0.48, 0.48, 0.49, 0.49, 0.49, 0.49, 0.5, 0.5, 0.5, 0.5, 0.51, 0.51, 0.51, 0.52, 0.52, 0.52, 0.52, 0.53, 0.53, 0.53, 0.53, 0.53, 0.54, 0.54, 0.54, 0.54, 0.54, 0.55, 0.55, 0.55, 0.55, 0.55, 0.56, 0.56, 0.56, 0.56, 0.57, 0.57, 0.57, 0.57, 0.57, 0.58, 0.58, 0.58, 0.58, 0.58, 0.58, 0.59, 0.59, 0.59, 0.59, 0.59, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.62, 0.62, 0.62, 0.62, 0.62, 0.62, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63, 0.64, 0.64, 0.64, 0.64, 0.64, 0.64]. Think about how Dew Point, Relative Humidity, Temperature, DNI, DHI influence solar_power. Please give me a forecast for the next 25 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Temperature, DNI, DHI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_97.pkl | external_data/ground_truth_data/ground_truth_data_97.pkl | external_data/context/context_97.pkl | external_data/constraint/constraint_97.pkl |
98 | electricity_prediction-min_load | I have historical GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle data and the corresponding solar_power data for the past 179 minutes. I require that the system load is maintained above a minimum of 0.31259741032628274 MW. Think about how GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 85 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle data and the corresponding solar_power data for the past 179 minutes. I require that the system load is maintained above a minimum of 0.31259741032628274 MW. The historical GHI data for the past 179 minutes is: [14.4, 15.6, 16.8, 18.0, 18.2, 18.4, 18.6, 18.8, 19.0, 20.4, 21.8, 23.2, 24.6, 26.0, 27.4, 28.8, 30.2, 31.6, 33.0, 34.6, 36.2, 37.8, 39.4, 41.0, 42.6, 44.2, 45.8, 47.4, 49.0, 50.8, 52.6, 54.4, 56.2, 58.0, 60.0, 62.0, 64.0, 66.0, 68.0, 70.0, 72.0, 74.0, 76.0, 78.0, 80.0, 82.0, 84.0, 86.0, 88.0, 95.0, 102.0, 109.0, 116.0, 123.0, 125.4, 127.8, 130.2, 132.6, 135.0, 137.6, 140.2, 142.8, 145.4, 148.0, 145.0, 142.0, 139.0, 136.0, 133.0, 135.4, 137.8, 140.2, 142.6, 145.0, 147.4, 149.8, 152.2, 154.6, 157.0, 165.8, 174.6, 183.4, 192.2, 201.0, 204.2, 207.4, 210.6, 213.8, 217.0, 218.8, 220.6, 222.4, 224.2, 226.0, 226.8, 227.6, 228.4, 229.2, 230.0, 231.0, 232.0, 233.0, 234.0, 235.0, 240.4, 245.8, 251.2, 256.6, 262.0, 264.0, 266.0, 268.0, 270.0, 272.0, 277.4, 282.8, 288.2, 293.6, 299.0, 300.4, 301.8, 303.2, 304.6, 306.0, 307.2, 308.4, 309.6, 310.8, 312.0, 315.4, 318.8, 322.2, 325.6, 329.0, 330.4, 331.8, 333.2, 334.6, 336.0, 336.0, 336.0, 336.0, 336.0, 336.0, 337.4, 338.8, 340.2, 341.6, 343.0, 345.6, 348.2, 350.8, 353.4, 356.0, 363.0, 370.0, 377.0, 384.0, 391.0, 392.2, 393.4, 394.6, 395.8, 397.0, 401.6, 406.2, 410.8, 415.4, 420.0, 421.8, 423.6, 425.4, 427.2, 429.0, 432.2, 435.4, 438.6, 441.8, 445.0]. The historical Temperature data for the past 179 minutes is: [21.04, 21.06, 21.08, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.12, 21.14, 21.16, 21.18, 21.2, 21.22, 21.24, 21.26, 21.28, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.32, 21.34, 21.36, 21.38, 21.4, 21.4, 21.4, 21.4, 21.4, 21.4, 21.42, 21.44, 21.46, 21.48, 21.5, 21.52, 21.54, 21.56, 21.58, 21.6, 21.6, 21.6, 21.6, 21.6, 21.6, 21.62, 21.64, 21.66, 21.68, 21.7, 21.7, 21.7, 21.7, 21.7, 21.7, 21.72, 21.74, 21.76, 21.78, 21.8, 21.82, 21.84, 21.86, 21.88, 21.9, 21.92, 21.94, 21.96, 21.98, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.02, 22.04, 22.06, 22.08, 22.1, 22.12, 22.14, 22.16, 22.18, 22.2, 22.2, 22.2, 22.2, 22.2, 22.2, 22.22, 22.24, 22.26, 22.28, 22.3, 22.32, 22.34, 22.36, 22.38, 22.4, 22.4, 22.4, 22.4, 22.4, 22.4, 22.42, 22.44, 22.46, 22.48, 22.5, 22.52, 22.54, 22.56, 22.58, 22.6, 22.6, 22.6, 22.6, 22.6, 22.6, 22.62, 22.64, 22.66, 22.68, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.72, 22.74, 22.76, 22.78, 22.8, 22.8, 22.8, 22.8, 22.8, 22.8, 22.82, 22.84, 22.86, 22.88, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.92, 22.94, 22.96, 22.98, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.02, 23.04, 23.06, 23.08, 23.1, 23.12, 23.14, 23.16, 23.18, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2]. The historical Relative Humidity data for the past 179 minutes is: [89.84, 89.73, 89.62, 89.51, 89.51, 89.51, 89.51, 89.51, 89.51, 89.4, 89.29, 89.19, 89.08, 88.97, 88.86, 88.75, 88.65, 88.54, 88.43, 88.43, 88.43, 88.43, 88.43, 88.43, 88.32, 88.21, 88.11, 88.0, 87.89, 87.55, 87.21, 86.88, 86.54, 86.2, 86.1, 85.99, 85.89, 85.78, 85.68, 85.58, 85.47, 85.37, 85.26, 85.16, 85.16, 85.16, 85.16, 85.16, 85.16, 85.06, 84.95, 84.85, 84.74, 84.64, 84.64, 84.64, 84.64, 84.64, 84.64, 84.54, 84.44, 84.33, 84.23, 84.13, 84.03, 83.93, 83.82, 83.72, 83.62, 83.52, 83.42, 83.31, 83.21, 83.11, 83.11, 83.11, 83.11, 83.11, 83.11, 83.01, 82.91, 82.81, 82.71, 82.61, 82.51, 82.41, 82.31, 82.21, 82.11, 81.49, 80.87, 80.24, 79.62, 79.0, 78.9, 78.81, 78.71, 78.62, 78.52, 78.44, 78.36, 78.28, 78.2, 78.12, 78.12, 78.12, 78.12, 78.12, 78.12, 78.03, 77.93, 77.84, 77.74, 77.65, 77.56, 77.46, 77.37, 77.27, 77.18, 77.18, 77.18, 77.18, 77.18, 77.18, 77.09, 77.0, 76.9, 76.81, 76.72, 76.72, 76.72, 76.72, 76.72, 76.72, 76.63, 76.54, 76.44, 76.35, 76.26, 76.26, 76.26, 76.26, 76.26, 76.26, 76.17, 76.08, 75.98, 75.89, 75.8, 75.43, 75.07, 74.7, 74.34, 73.97, 73.88, 73.79, 73.71, 73.62, 73.53, 73.53, 73.53, 73.53, 73.53, 73.53, 73.44, 73.35, 73.27, 73.18, 73.09, 73.0, 72.91, 72.83, 72.74, 72.65, 72.65, 72.65, 72.65, 72.65, 72.65]. The historical Dew Point data for the past 179 minutes is: [19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.24, 19.18, 19.12, 19.06, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 18.88, 18.76, 18.64, 18.52, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.32, 18.24, 18.16, 18.08, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0]. The historical Solar Zenith Angle data for the past 179 minutes is: [88.14, 87.97, 87.81, 87.65, 87.49, 87.32, 87.16, 86.99, 86.83, 86.66, 86.49, 86.32, 86.15, 85.98, 85.81, 85.64, 85.46, 85.29, 85.12, 84.95, 84.78, 84.6, 84.43, 84.26, 84.08, 83.91, 83.73, 83.56, 83.38, 83.2, 83.03, 82.85, 82.68, 82.5, 82.32, 82.14, 81.97, 81.79, 81.61, 81.43, 81.25, 81.07, 80.89, 80.71, 80.53, 80.35, 80.17, 79.99, 79.81, 79.63, 79.45, 79.27, 79.09, 78.91, 78.73, 78.55, 78.36, 78.18, 78.0, 77.82, 77.63, 77.45, 77.26, 77.08, 76.9, 76.72, 76.53, 76.35, 76.17, 75.99, 75.8, 75.62, 75.43, 75.25, 75.06, 74.88, 74.69, 74.51, 74.32, 74.14, 73.95, 73.77, 73.58, 73.4, 73.21, 73.03, 72.84, 72.66, 72.47, 72.28, 72.1, 71.91, 71.73, 71.54, 71.35, 71.16, 70.98, 70.79, 70.6, 70.41, 70.23, 70.04, 69.86, 69.67, 69.48, 69.29, 69.11, 68.92, 68.73, 68.54, 68.35, 68.17, 67.98, 67.79, 67.6, 67.41, 67.23, 67.04, 66.85, 66.66, 66.47, 66.29, 66.1, 65.91, 65.72, 65.53, 65.35, 65.16, 64.97, 64.78, 64.59, 64.4, 64.21, 64.02, 63.83, 63.64, 63.46, 63.27, 63.08, 62.89, 62.7, 62.51, 62.32, 62.13, 61.94, 61.75, 61.57, 61.38, 61.19, 61.0, 60.81, 60.62, 60.43, 60.24, 60.05, 59.86, 59.68, 59.49, 59.3, 59.11, 58.92, 58.73, 58.54, 58.35, 58.16, 57.97, 57.78, 57.59, 57.4, 57.21, 57.02, 56.84, 56.65, 56.46, 56.27, 56.08, 55.9, 55.71, 55.52]. The historical solar_power data for the past 179 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.11, 0.11, 0.12, 0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.15, 0.15, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.19, 0.19, 0.19, 0.2, 0.2, 0.21, 0.21, 0.21, 0.21, 0.21, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.23, 0.23, 0.23, 0.24, 0.24, 0.24, 0.24, 0.24, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.26, 0.26, 0.26, 0.26, 0.27, 0.27, 0.27, 0.28, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.33, 0.33, 0.34, 0.34, 0.34, 0.34, 0.35]. Think about how GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 85 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of GHI, Temperature, Relative Humidity, Dew Point, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_98.pkl | external_data/ground_truth_data/ground_truth_data_98.pkl | external_data/context/context_98.pkl | external_data/constraint/constraint_98.pkl |
99 | electricity_prediction-min_load | I have historical Wind Speed, Relative Humidity, Temperature data and the corresponding load_power data for the past 92 minutes. I require that the system load is maintained above a minimum of 0.968046951921384 MW. Think about how Wind Speed, Relative Humidity, Temperature influence load_power. Please give me a forecast for the next 78 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity, Temperature data and the corresponding load_power data for the past 92 minutes. I require that the system load is maintained above a minimum of 0.968046951921384 MW. The historical Wind Speed data for the past 92 minutes is: [3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3]. The historical Relative Humidity data for the past 92 minutes is: [58.15, 58.07, 57.99, 57.91, 57.83, 57.75, 57.67, 57.59, 57.51, 57.43, 57.35, 57.41, 57.47, 57.52, 57.58, 57.64, 57.56, 57.48, 57.4, 57.32, 57.24, 57.24, 57.24, 57.24, 57.24, 57.24, 57.16, 57.08, 57.0, 56.92, 56.84, 56.76, 56.68, 56.6, 56.52, 56.44, 56.36, 56.28, 56.21, 56.13, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 56.05, 55.97, 55.89, 55.82, 55.74, 55.66, 55.66, 55.66, 55.66, 55.66, 55.66, 55.58, 55.51, 55.43, 55.36, 55.28, 55.28, 55.28, 55.28, 55.28, 55.28, 55.26, 55.24, 55.23, 55.21, 55.19, 55.19, 55.19, 55.19, 55.19, 55.19, 55.11, 55.04, 54.96, 54.89, 54.81, 54.81, 54.81, 54.81, 54.81, 54.81, 54.73]. The historical Temperature data for the past 92 minutes is: [4.3, 4.32, 4.34, 4.36, 4.38, 4.4, 4.42, 4.44, 4.46, 4.48, 4.5, 4.52, 4.54, 4.56, 4.58, 4.6, 4.62, 4.64, 4.66, 4.68, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.72, 4.74, 4.76, 4.78, 4.8, 4.82, 4.84, 4.86, 4.88, 4.9, 4.92, 4.94, 4.96, 4.98, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.02, 5.04, 5.06, 5.08, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.12, 5.14, 5.16, 5.18, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.22, 5.24, 5.26, 5.28, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.32, 5.34, 5.36, 5.38, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.42]. The historical load_power data for the past 92 minutes is: [1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]. Think about how Wind Speed, Relative Humidity, Temperature influence load_power. Please give me a forecast for the next 78 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_99.pkl | external_data/ground_truth_data/ground_truth_data_99.pkl | external_data/context/context_99.pkl | external_data/constraint/constraint_99.pkl |
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