{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/qiaozhang/miniconda3/envs/demand-forecasting/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apikey still available, logged in\n"
     ]
    }
   ],
   "source": [
    "# To call functions outside of this folder\n",
    "import sys  \n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "# Load libraries \n",
    "import pandas as pd\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load main demand forecasting class\n",
    "from src.main import DemandForecasting\n",
    "\n",
    "df = DemandForecasting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.read_csv('../data/raw/energy_consmption/AEP_hourly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.to_csv('../data/energy_consumption.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>y</th>\n",
       "      <th>sku</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>2019-01-20</td>\n",
       "      <td>3302854.0</td>\n",
       "      <td>engy_use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>3235220.0</td>\n",
       "      <td>engy_use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-16</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>3382229.0</td>\n",
       "      <td>engy_use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>3442532.0</td>\n",
       "      <td>engy_use</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime          y       sku\n",
       "2019-01-20 2019-01-20  3302854.0  engy_use\n",
       "2020-01-05 2020-01-05  3235220.0  engy_use\n",
       "2020-02-16 2020-02-16  3382229.0  engy_use\n",
       "2023-01-01 2023-01-01  3442532.0  engy_use"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[ts.y > 3.2e+06]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.loc[pd.to_datetime('2023-01-01'), 'y'] = 2842532.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts['y'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = ts['Datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = pd.to_datetime(ts.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.resample('W').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = ts.index + pd.DateOffset(years=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AEP_MW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-10-03</th>\n",
       "      <td>933991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-10</th>\n",
       "      <td>2332037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-17</th>\n",
       "      <td>2378184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-24</th>\n",
       "      <td>2379237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10-31</th>\n",
       "      <td>2325624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-08</th>\n",
       "      <td>2707980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-15</th>\n",
       "      <td>2799780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-22</th>\n",
       "      <td>2616533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-29</th>\n",
       "      <td>2588782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-05</th>\n",
       "      <td>1488102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>723 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               AEP_MW\n",
       "Datetime             \n",
       "2009-10-03   933991.0\n",
       "2009-10-10  2332037.0\n",
       "2009-10-17  2378184.0\n",
       "2009-10-24  2379237.0\n",
       "2009-10-31  2325624.0\n",
       "...               ...\n",
       "2023-07-08  2707980.0\n",
       "2023-07-15  2799780.0\n",
       "2023-07-22  2616533.0\n",
       "2023-07-29  2588782.0\n",
       "2023-08-05  1488102.0\n",
       "\n",
       "[723 rows x 1 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts[ts.index <= pd.to_datetime('2023-10-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.rename(columns={'Datetime':'datetime', 'AEP_MW':'y'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts['sku'] = 'engy_use'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = pd.to_datetime(ts['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.resample('M').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-01-05 00:00:00')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts[ts.index >= pd.to_datetime('2019')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-08-05 00:00:00')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 3)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = pd.date_range(start='2018-12-25', end='2023-08-05', freq='W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts['datetime'] = ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.infer_freq(ts['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'y', 'sku'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.to_csv('../data/demand_forecasting_demo_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
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       "      <th>y</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>264</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>164</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>217</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>195</td>\n",
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       "      <td>Item_C</td>\n",
       "      <td>203</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>272</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>311</td>\n",
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       "      <th>28</th>\n",
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       "      <td>Item_C</td>\n",
       "      <td>363</td>\n",
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       "      <th>33</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>228</td>\n",
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       "      <th>38</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>232</td>\n",
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       "      <th>43</th>\n",
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       "      <th>48</th>\n",
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       "      <td>247</td>\n",
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       "      <th>63</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>221</td>\n",
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       "      <th>68</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>272</td>\n",
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       "      <th>73</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>256</td>\n",
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       "      <th>78</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>227</td>\n",
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       "      <th>83</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>151</td>\n",
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       "      <th>88</th>\n",
       "      <td>2019-09-01</td>\n",
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       "      <td>2019-10-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>204</td>\n",
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       "      <th>98</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>187</td>\n",
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       "      <th>103</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>90</td>\n",
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       "      <th>108</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>170</td>\n",
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       "      <th>113</th>\n",
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       "      <td>Item_C</td>\n",
       "      <td>169</td>\n",
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       "      <td>Item_C</td>\n",
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       "      <td>126</td>\n",
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       "      <th>138</th>\n",
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       "      <td>199</td>\n",
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       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>107</td>\n",
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       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>215</td>\n",
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       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>Item_C</td>\n",
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       "      <th>188</th>\n",
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       "      <td>Item_C</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>Item_C</td>\n",
       "      <td>199</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "      datetime     sku    y\n",
       "2   2018-01-01  Item_C  264\n",
       "5   2018-02-01  Item_C  164\n",
       "8   2018-03-01  Item_C  217\n",
       "11  2018-04-01  Item_C  195\n",
       "14  2018-05-01  Item_C  229\n",
       "17  2018-06-01  Item_C  203\n",
       "20  2018-07-01  Item_C  272\n",
       "24  2018-08-01  Item_C  311\n",
       "28  2018-09-01  Item_C  363\n",
       "33  2018-10-01  Item_C  228\n",
       "38  2018-11-01  Item_C  232\n",
       "43  2018-12-01  Item_C  171\n",
       "48  2019-01-01  Item_C  247\n",
       "53  2019-02-01  Item_C  195\n",
       "58  2019-03-01  Item_C  234\n",
       "63  2019-04-01  Item_C  221\n",
       "68  2019-05-01  Item_C  272\n",
       "73  2019-06-01  Item_C  256\n",
       "78  2019-07-01  Item_C  227\n",
       "83  2019-08-01  Item_C  151\n",
       "88  2019-09-01  Item_C  151\n",
       "93  2019-10-01  Item_C  204\n",
       "98  2019-11-01  Item_C  187\n",
       "103 2019-12-01  Item_C   90\n",
       "108 2020-01-01  Item_C  170\n",
       "113 2020-02-01  Item_C  169\n",
       "118 2020-03-01  Item_C  208\n",
       "123 2020-04-01  Item_C   87\n",
       "128 2020-05-01  Item_C  126\n",
       "133 2020-06-01  Item_C  137\n",
       "138 2020-07-01  Item_C  164\n",
       "143 2020-08-01  Item_C  170\n",
       "148 2020-09-01  Item_C  192\n",
       "153 2020-10-01  Item_C  184\n",
       "158 2020-11-01  Item_C  199\n",
       "163 2020-12-01  Item_C  107\n",
       "168 2021-01-01  Item_C  254\n",
       "173 2021-02-01  Item_C  170\n",
       "178 2021-03-01  Item_C  215\n",
       "183 2021-04-01  Item_C  143\n",
       "188 2021-05-01  Item_C  213\n",
       "193 2021-06-01  Item_C  243\n",
       "198 2021-07-01  Item_C  199"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Item_A', 'Item_B', 'Item_C', 'Item_D', 'Item_E'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.sku.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>380</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2022-11-06</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2022-11-13</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2022-11-20</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>Item_F</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       datetime     sku    y\n",
       "377  2020-06-07  Item_F    0\n",
       "378  2020-06-14  Item_F   20\n",
       "379  2020-06-21  Item_F   25\n",
       "380  2020-06-28  Item_F    0\n",
       "381  2020-07-05  Item_F    0\n",
       "..          ...     ...  ...\n",
       "503  2022-11-06  Item_F   30\n",
       "504  2022-11-13  Item_F    0\n",
       "505  2022-11-20  Item_F   70\n",
       "506  2022-11-27  Item_F  100\n",
       "507  2022-12-04  Item_F   50\n",
       "\n",
       "[131 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Item_F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
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       "      <td>11</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>2018-05-13</td>\n",
       "      <td>6</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>4</td>\n",
       "      <td>Item_F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>8</td>\n",
       "      <td>Item_F</td>\n",
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       "      <th>522</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2022-11-06</td>\n",
       "      <td>30</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2022-11-13</td>\n",
       "      <td>0</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>2022-11-20</td>\n",
       "      <td>70</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>2022-11-27</td>\n",
       "      <td>100</td>\n",
       "      <td>Item_F</td>\n",
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       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>50</td>\n",
       "      <td>Item_F</td>\n",
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       "  </tbody>\n",
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       "<p>240 rows × 3 columns</p>\n",
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      "text/plain": [
       "       datetime    y     sku\n",
       "518  2018-05-06   11  Item_F\n",
       "519  2018-05-13    6  Item_F\n",
       "520  2018-05-20    4  Item_F\n",
       "521  2018-05-27    8  Item_F\n",
       "522  2018-06-03    1  Item_F\n",
       "..          ...  ...     ...\n",
       "753  2022-11-06   30  Item_F\n",
       "754  2022-11-13    0  Item_F\n",
       "755  2022-11-20   70  Item_F\n",
       "756  2022-11-27  100  Item_F\n",
       "757  2022-12-04   50  Item_F\n",
       "\n",
       "[240 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.concat([ts, Item_F])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.to_csv('../data/demand_forecasting_demo_data_new.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'itemf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb#X21sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m itemf\n",
      "\u001b[0;31mNameError\u001b[0m: name 'itemf' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_predict is 0, force run_test to be true\n",
      "characteristic not provided, running profiling\n",
      "apikey still available, logged in\n",
      "Start profiling, note, predictability been disabled\n",
      "Change point detection\n",
      "predictability temporarily using order_quantity predictability\n",
      "apikey still available, logged in\n",
      "Start profiling, note, predictability been disabled\n",
      "Change point detection\n",
      "profiling completed, data characteristic is  fuzzy\n",
      "callindg model: prophet_plus\n",
      "has_idsc_model\n",
      "apikey still available, logged in\n",
      "callindg model: ceif_plus\n",
      "has_idsc_model\n",
      "IDSC ceif\n",
      "apikey still available, logged in\n",
      "IDSC ceif completed\n"
     ]
    }
   ],
   "source": [
    "# Step 1 - evaluate RMSE\n",
    "res = df.forecast(ts, 0, model='all', run_test=True)\n",
    "\n",
    "# Step 2 - forecast\n",
    "# res = df.forecast(ts, 26, model='prophet_plus', characteristic='fuzzy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-05-01', '2022-05-08', '2022-05-15', '2022-05-22',\n",
       "               '2022-05-29', '2022-06-05', '2022-06-12', '2022-06-19',\n",
       "               '2022-06-26', '2022-07-03', '2022-07-10', '2022-07-17',\n",
       "               '2022-07-24', '2022-07-31', '2022-08-07', '2022-08-14',\n",
       "               '2022-08-21', '2022-08-28', '2022-09-04', '2022-09-11',\n",
       "               '2022-09-18', '2022-09-25', '2022-10-02', '2022-10-09',\n",
       "               '2022-10-16', '2022-10-23', '2022-10-30', '2022-11-06',\n",
       "               '2022-11-13', '2022-11-20', '2022-11-27', '2022-12-04',\n",
       "               '2022-12-11', '2022-12-18', '2022-12-25', '2023-01-01',\n",
       "               '2023-01-08', '2023-01-15', '2023-01-22', '2023-01-29',\n",
       "               '2023-02-05', '2023-02-12', '2023-02-19', '2023-02-26',\n",
       "               '2023-03-05', '2023-03-12', '2023-03-19', '2023-03-26',\n",
       "               '2023-04-02', '2023-04-09', '2023-04-16', '2023-04-23'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res['forecast'][0]['test'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-05-01     0\n",
       "2022-05-08     0\n",
       "2022-05-15    10\n",
       "2022-05-22     7\n",
       "2022-05-29     9\n",
       "2022-06-05     6\n",
       "2022-06-12     5\n",
       "2022-06-19    35\n",
       "2022-06-26    20\n",
       "2022-07-03     0\n",
       "2022-07-10     5\n",
       "2022-07-17     5\n",
       "2022-07-24     9\n",
       "2022-07-31    14\n",
       "2022-08-07    20\n",
       "2022-08-14    10\n",
       "2022-08-21    10\n",
       "2022-08-28     1\n",
       "2022-09-04    15\n",
       "2022-09-11    22\n",
       "2022-09-18    10\n",
       "2022-09-25    10\n",
       "2022-10-02    20\n",
       "2022-10-09     0\n",
       "2022-10-16     0\n",
       "2022-10-23     0\n",
       "2022-10-30    15\n",
       "2022-11-06    10\n",
       "2022-11-13     0\n",
       "2022-11-20    10\n",
       "2022-11-27    10\n",
       "2022-12-04     0\n",
       "2022-12-11     0\n",
       "2022-12-18     0\n",
       "2022-12-25     7\n",
       "2023-01-01    10\n",
       "2023-01-08    10\n",
       "2023-01-15     0\n",
       "2023-01-22     5\n",
       "2023-01-29     0\n",
       "2023-02-05     7\n",
       "2023-02-12     2\n",
       "2023-02-19     0\n",
       "2023-02-26    20\n",
       "2023-03-05    13\n",
       "2023-03-12    10\n",
       "2023-03-19     0\n",
       "2023-03-26     0\n",
       "2023-04-02    10\n",
       "2023-04-09     8\n",
       "2023-04-16    10\n",
       "2023-04-23     5\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[ts.index > pd.to_datetime('2018')].loc[res['forecast'][0]['test'].index]['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>2023-04-23</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-05-07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-05-21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-05-28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-06-04</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-06-11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-06-18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-06-25</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-07-02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-07-09</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-07-16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023-07-23</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-08-06</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2023-08-13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2023-08-20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2023-08-27</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2023-09-03</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2023-09-10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2023-09-17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2023-09-24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2023-10-15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime   y\n",
       "0  2023-04-23   0\n",
       "1  2023-04-30  18\n",
       "2  2023-05-07  12\n",
       "3  2023-05-14  10\n",
       "4  2023-05-21  12\n",
       "5  2023-05-28   0\n",
       "6  2023-06-04  11\n",
       "7  2023-06-11   0\n",
       "8  2023-06-18  11\n",
       "9  2023-06-25  13\n",
       "10 2023-07-02   0\n",
       "11 2023-07-09  18\n",
       "12 2023-07-16  12\n",
       "13 2023-07-23  10\n",
       "14 2023-07-30  12\n",
       "15 2023-08-06   0\n",
       "16 2023-08-13  11\n",
       "17 2023-08-20   0\n",
       "18 2023-08-27  11\n",
       "19 2023-09-03  13\n",
       "20 2023-09-10   0\n",
       "21 2023-09-17  18\n",
       "22 2023-09-24  12\n",
       "23 2023-10-01  10\n",
       "24 2023-10-08  12\n",
       "25 2023-10-15   0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(res['forecast'][0]['forecast'], columns=['datetime', 'y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prophet_plus\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'interm_scores'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      3\u001b[0m \u001b[39m# print(r['RMSE'])\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39m# print(r['order_quantity_RMSE'])\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[39mprint\u001b[39m(r[\u001b[39m'\u001b[39;49m\u001b[39minterm_scores\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m      6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m________\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m r[\u001b[39m'\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mplot(title\u001b[39m=\u001b[39mr[\u001b[39m'\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m-test\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'interm_scores'"
     ]
    }
   ],
   "source": [
    "for r in res['forecast']:\n",
    "    print(r['model'])\n",
    "    # print(r['RMSE'])\n",
    "    # print(r['order_quantity_RMSE'])\n",
    "    print(r['interm_scores'])\n",
    "    print('________')\n",
    "    r['test'].plot(title=r['model'] + '-test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb Cell 10\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/qiaozhang/Desktop/sentient-dev/snr_demand-forecasting/notebooks/test.ipynb#X12sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m res[\u001b[39m4\u001b[39;49m][\u001b[39m'\u001b[39m\u001b[39mtest_raw\u001b[39m\u001b[39m'\u001b[39m]\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "res[4]['test_raw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11:17:02 - cmdstanpy - INFO - Chain [1] start processing\n",
      "11:17:02 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "callindg model: prophet\n",
      "            ds     trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
      "260 2023-04-30  8.921487   -7.908574   21.012119     8.921487     8.921487   \n",
      "261 2023-05-07  8.931226   -6.567967   22.604438     8.931226     8.931226   \n",
      "262 2023-05-14  8.940965   -8.081720   21.526347     8.940905     8.941007   \n",
      "263 2023-05-21  8.950703   -9.985601   19.493650     8.950543     8.950831   \n",
      "264 2023-05-28  8.960442   -8.813073   20.526393     8.960129     8.960702   \n",
      "265 2023-06-04  8.970180   -6.966844   23.563550     8.969667     8.970604   \n",
      "266 2023-06-11  8.979919   -4.608789   25.118377     8.979231     8.980524   \n",
      "267 2023-06-18  8.989657   -4.180691   25.485273     8.988761     8.990488   \n",
      "268 2023-06-25  8.999396   -4.686704   24.961761     8.998250     9.000445   \n",
      "269 2023-07-02  9.009134   -5.880469   23.125865     9.007781     9.010505   \n",
      "270 2023-07-09  9.018873   -5.090580   23.769587     9.017264     9.020526   \n",
      "271 2023-07-16  9.028611   -4.761036   25.748781     9.026645     9.030525   \n",
      "272 2023-07-23  9.038350   -4.907963   24.736967     9.036085     9.040552   \n",
      "273 2023-07-30  9.048088   -4.570258   23.745148     9.045508     9.050722   \n",
      "274 2023-08-06  9.057827   -5.827376   22.932313     9.054935     9.060731   \n",
      "275 2023-08-13  9.067565   -4.220147   24.956558     9.064331     9.070780   \n",
      "276 2023-08-20  9.077304   -0.864349   29.034652     9.073628     9.081006   \n",
      "277 2023-08-27  9.087042    3.470909   32.343086     9.082870     9.091054   \n",
      "278 2023-09-03  9.096781    1.198728   30.637736     9.092259     9.101139   \n",
      "279 2023-09-10  9.106520   -1.561115   27.531066     9.101419     9.111209   \n",
      "280 2023-09-17  9.116258   -6.184726   24.202682     9.110719     9.121436   \n",
      "281 2023-09-24  9.125997   -4.579644   23.752222     9.120080     9.131616   \n",
      "282 2023-10-01  9.135735   -5.178492   25.012735     9.129285     9.141805   \n",
      "283 2023-10-08  9.145474   -5.666373   22.611290     9.138581     9.152031   \n",
      "284 2023-10-15  9.155212   -6.963614   21.842661     9.147855     9.162345   \n",
      "285 2023-10-22  9.164951   -6.763386   23.055701     9.157131     9.172489   \n",
      "286 2023-10-29  9.174689   -5.170320   24.787756     9.166426     9.182695   \n",
      "287 2023-11-05  9.184428   -2.337336   27.885403     9.175632     9.192803   \n",
      "288 2023-11-12  9.194166   -1.331529   29.762405     9.184876     9.203052   \n",
      "289 2023-11-19  9.203905   -2.375162   27.729052     9.194147     9.213316   \n",
      "\n",
      "     additive_terms  additive_terms_lower  additive_terms_upper    yearly  \\\n",
      "260       -2.521643             -2.521643             -2.521643 -2.521643   \n",
      "261       -1.628433             -1.628433             -1.628433 -1.628433   \n",
      "262       -2.544671             -2.544671             -2.544671 -2.544671   \n",
      "263       -3.980016             -3.980016             -3.980016 -3.980016   \n",
      "264       -3.655285             -3.655285             -3.655285 -3.655285   \n",
      "265       -1.186016             -1.186016             -1.186016 -1.186016   \n",
      "266        1.337140              1.337140              1.337140  1.337140   \n",
      "267        1.789455              1.789455              1.789455  1.789455   \n",
      "268        0.451573              0.451573              0.451573  0.451573   \n",
      "269       -0.531397             -0.531397             -0.531397 -0.531397   \n",
      "270        0.094160              0.094160              0.094160  0.094160   \n",
      "271        1.226812              1.226812              1.226812  1.226812   \n",
      "272        1.064831              1.064831              1.064831  1.064831   \n",
      "273       -0.269867             -0.269867             -0.269867 -0.269867   \n",
      "274       -0.559179             -0.559179             -0.559179 -0.559179   \n",
      "275        1.853473              1.853473              1.853473  1.853473   \n",
      "276        5.774049              5.774049              5.774049  5.774049   \n",
      "277        8.102311              8.102311              8.102311  8.102311   \n",
      "278        7.022347              7.022347              7.022347  7.022347   \n",
      "279        3.785691              3.785691              3.785691  3.785691   \n",
      "280        1.145548              1.145548              1.145548  1.145548   \n",
      "281        0.423666              0.423666              0.423666  0.423666   \n",
      "282        0.590499              0.590499              0.590499  0.590499   \n",
      "283        0.116618              0.116618              0.116618  0.116618   \n",
      "284       -0.921952             -0.921952             -0.921952 -0.921952   \n",
      "285       -1.023065             -1.023065             -1.023065 -1.023065   \n",
      "286        0.705503              0.705503              0.705503  0.705503   \n",
      "287        3.244306              3.244306              3.244306  3.244306   \n",
      "288        4.575861              4.575861              4.575861  4.575861   \n",
      "289        3.595682              3.595682              3.595682  3.595682   \n",
      "\n",
      "     yearly_lower  yearly_upper  multiplicative_terms  \\\n",
      "260     -2.521643     -2.521643                   0.0   \n",
      "261     -1.628433     -1.628433                   0.0   \n",
      "262     -2.544671     -2.544671                   0.0   \n",
      "263     -3.980016     -3.980016                   0.0   \n",
      "264     -3.655285     -3.655285                   0.0   \n",
      "265     -1.186016     -1.186016                   0.0   \n",
      "266      1.337140      1.337140                   0.0   \n",
      "267      1.789455      1.789455                   0.0   \n",
      "268      0.451573      0.451573                   0.0   \n",
      "269     -0.531397     -0.531397                   0.0   \n",
      "270      0.094160      0.094160                   0.0   \n",
      "271      1.226812      1.226812                   0.0   \n",
      "272      1.064831      1.064831                   0.0   \n",
      "273     -0.269867     -0.269867                   0.0   \n",
      "274     -0.559179     -0.559179                   0.0   \n",
      "275      1.853473      1.853473                   0.0   \n",
      "276      5.774049      5.774049                   0.0   \n",
      "277      8.102311      8.102311                   0.0   \n",
      "278      7.022347      7.022347                   0.0   \n",
      "279      3.785691      3.785691                   0.0   \n",
      "280      1.145548      1.145548                   0.0   \n",
      "281      0.423666      0.423666                   0.0   \n",
      "282      0.590499      0.590499                   0.0   \n",
      "283      0.116618      0.116618                   0.0   \n",
      "284     -0.921952     -0.921952                   0.0   \n",
      "285     -1.023065     -1.023065                   0.0   \n",
      "286      0.705503      0.705503                   0.0   \n",
      "287      3.244306      3.244306                   0.0   \n",
      "288      4.575861      4.575861                   0.0   \n",
      "289      3.595682      3.595682                   0.0   \n",
      "\n",
      "     multiplicative_terms_lower  multiplicative_terms_upper       yhat  \n",
      "260                         0.0                         0.0   6.399845  \n",
      "261                         0.0                         0.0   7.302793  \n",
      "262                         0.0                         0.0   6.396294  \n",
      "263                         0.0                         0.0   4.970687  \n",
      "264                         0.0                         0.0   5.305157  \n",
      "265                         0.0                         0.0   7.784164  \n",
      "266                         0.0                         0.0  10.317059  \n",
      "267                         0.0                         0.0  10.779112  \n",
      "268                         0.0                         0.0   9.450969  \n",
      "269                         0.0                         0.0   8.477737  \n",
      "270                         0.0                         0.0   9.113033  \n",
      "271                         0.0                         0.0  10.255423  \n",
      "272                         0.0                         0.0  10.103181  \n",
      "273                         0.0                         0.0   8.778221  \n",
      "274                         0.0                         0.0   8.498648  \n",
      "275                         0.0                         0.0  10.921038  \n",
      "276                         0.0                         0.0  14.851353  \n",
      "277                         0.0                         0.0  17.189353  \n",
      "278                         0.0                         0.0  16.119128  \n",
      "279                         0.0                         0.0  12.892211  \n",
      "280                         0.0                         0.0  10.261806  \n",
      "281                         0.0                         0.0   9.549662  \n",
      "282                         0.0                         0.0   9.726234  \n",
      "283                         0.0                         0.0   9.262091  \n",
      "284                         0.0                         0.0   8.233260  \n",
      "285                         0.0                         0.0   8.141886  \n",
      "286                         0.0                         0.0   9.880192  \n",
      "287                         0.0                         0.0  12.428733  \n",
      "288                         0.0                         0.0  13.770027  \n",
      "289                         0.0                         0.0  12.799587  \n"
     ]
    }
   ],
   "source": [
    "# Step 2 - forecast\n",
    "res = df.forecast(ts, 30, model='prophet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-04-23</th>\n",
       "      <td>6.399845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-30</th>\n",
       "      <td>7.302793</td>\n",
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       "    <tr>\n",
       "      <th>2023-05-07</th>\n",
       "      <td>6.396294</td>\n",
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       "    <tr>\n",
       "      <th>2023-05-14</th>\n",
       "      <td>4.970687</td>\n",
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       "    <tr>\n",
       "      <th>2023-05-21</th>\n",
       "      <td>5.305157</td>\n",
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       "    <tr>\n",
       "      <th>2023-05-28</th>\n",
       "      <td>7.784164</td>\n",
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       "    <tr>\n",
       "      <th>2023-06-04</th>\n",
       "      <td>10.317059</td>\n",
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       "    <tr>\n",
       "      <th>2023-06-11</th>\n",
       "      <td>10.779112</td>\n",
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       "      <th>2023-06-18</th>\n",
       "      <td>9.450969</td>\n",
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       "    <tr>\n",
       "      <th>2023-06-25</th>\n",
       "      <td>8.477737</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-02</th>\n",
       "      <td>9.113033</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-09</th>\n",
       "      <td>10.255423</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-16</th>\n",
       "      <td>10.103181</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-23</th>\n",
       "      <td>8.778221</td>\n",
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       "    <tr>\n",
       "      <th>2023-07-30</th>\n",
       "      <td>8.498648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-06</th>\n",
       "      <td>10.921038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-13</th>\n",
       "      <td>14.851353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-20</th>\n",
       "      <td>17.189353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-27</th>\n",
       "      <td>16.119128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-03</th>\n",
       "      <td>12.892211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-10</th>\n",
       "      <td>10.261806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-17</th>\n",
       "      <td>9.549662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24</th>\n",
       "      <td>9.726234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>9.262091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-08</th>\n",
       "      <td>8.233260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-15</th>\n",
       "      <td>8.141886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-22</th>\n",
       "      <td>9.880192</td>\n",
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       "    <tr>\n",
       "      <th>2023-10-29</th>\n",
       "      <td>12.428733</td>\n",
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       "    <tr>\n",
       "      <th>2023-11-05</th>\n",
       "      <td>13.770027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-12</th>\n",
       "      <td>12.799587</td>\n",
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      ],
      "text/plain": [
       "                    y\n",
       "2023-04-23   6.399845\n",
       "2023-04-30   7.302793\n",
       "2023-05-07   6.396294\n",
       "2023-05-14   4.970687\n",
       "2023-05-21   5.305157\n",
       "2023-05-28   7.784164\n",
       "2023-06-04  10.317059\n",
       "2023-06-11  10.779112\n",
       "2023-06-18   9.450969\n",
       "2023-06-25   8.477737\n",
       "2023-07-02   9.113033\n",
       "2023-07-09  10.255423\n",
       "2023-07-16  10.103181\n",
       "2023-07-23   8.778221\n",
       "2023-07-30   8.498648\n",
       "2023-08-06  10.921038\n",
       "2023-08-13  14.851353\n",
       "2023-08-20  17.189353\n",
       "2023-08-27  16.119128\n",
       "2023-09-03  12.892211\n",
       "2023-09-10  10.261806\n",
       "2023-09-17   9.549662\n",
       "2023-09-24   9.726234\n",
       "2023-10-01   9.262091\n",
       "2023-10-08   8.233260\n",
       "2023-10-15   8.141886\n",
       "2023-10-22   9.880192\n",
       "2023-10-29  12.428733\n",
       "2023-11-05  13.770027\n",
       "2023-11-12  12.799587"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[0]['forecast']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'forecasted'}>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res[0]['forecast'].plot(title='forecasted')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "demand-forecasting",
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