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autogluon
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fev_datasets

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Time Series Forecasting
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  ---
 
 
 
 
 
 
 
 
 
 
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  dataset_info:
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  - config_name: epf_electricity_be
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  features:
@@ -404,3 +414,64 @@ configs:
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  - split: train
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  path: proenfo/spain/train-*
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ annotations_creators:
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+ - no-annotation
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+ license: other
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - time-series-forecasting
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+ task_ids:
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+ - univariate-time-series-forecasting
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+ - multivariate-time-series-forecasting
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  dataset_info:
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  - config_name: epf_electricity_be
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  features:
 
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  - split: train
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  path: proenfo/spain/train-*
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  ---
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+
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+ ## Forecast evaluation datasets
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+
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+ This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models.
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+
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+ The datasets follow a format that is compatible with the [`fev`](https://github.com/autogluon/fev) package.
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+
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+ ## Data format and usage
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+
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+ Each dataset satisfies the following schema:
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+ - each dataset entry (=row) represents a single univariate or multivariate time series
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+ - each entry contains
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+ - 1/ a field of type `Sequence(timestamp)` that contains the timestamps of observations
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+ - 2/ at least one field of type `Sequence(float)` that can be used as the target time series or dynamic covariates
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+ - 3/ a field of type `string` that contains the unique ID of each time series
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+ - all fields of type `Sequence` have the same length
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+
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+ Datasets can be loaded using the [🤗 `datasets`](https://huggingface.co/docs/datasets/en/index) library.
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+
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+ ```python
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+ import datasets
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+
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+ ds = datasets.load_dataset("autogluon/fev_datasets", "epf_electricity_de", split="train")
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+ ds.set_format("numpy") # sequences returned as numpy arrays
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+ ```
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+ Example entry in the `epf_electricity_de` dataset
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+ ```python
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+ >>> ds[0]
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+ {'id': 'DE',
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+ 'timestamp': array(['2012-01-09T00:00:00.000000', '2012-01-09T01:00:00.000000',
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+ '2012-01-09T02:00:00.000000', ..., '2017-12-31T21:00:00.000000',
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+ '2017-12-31T22:00:00.000000', '2017-12-31T23:00:00.000000'],
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+ dtype='datetime64[us]'),
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+ 'target': array([34.97, 33.43, 32.74, ..., 5.3 , 1.86, -0.92], dtype=float32),
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+ 'Ampirion Load Forecast': array([16382. , 15410.5, 15595. , ..., 15715. , 15876. , 15130. ],
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+ dtype=float32),
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+ 'PV+Wind Forecast': array([ 3569.5276, 3315.275 , 3107.3076, ..., 29653.008 , 29520.33 ,
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+ 29466.408 ], dtype=float32)}
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+ ```
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+
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+ For more details about the dataset format and usage, check out the [`fev` documentation on GitHub](https://github.com/autogluon/fev?tab=readme-ov-file#tutorials).
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+
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+ ## Dataset statistics
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+
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+ | config | freq | # items | # obs | # dynamic cols | # static cols | homepage | license | citation |
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+ |:----------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|
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+ | `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [Apache 2.0](https://github.com/jeslago/epftoolbox/blob/a93dee7fd784993883374b211a021b706e80a433/LICENSE) | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [Apache 2.0](https://github.com/jeslago/epftoolbox/blob/a93dee7fd784993883374b211a021b706e80a433/LICENSE) | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [Apache 2.0](https://github.com/jeslago/epftoolbox/blob/a93dee7fd784993883374b211a021b706e80a433/LICENSE) | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [Apache 2.0](https://github.com/jeslago/epftoolbox/blob/a93dee7fd784993883374b211a021b706e80a433/LICENSE) | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [Apache 2.0](https://github.com/jeslago/epftoolbox/blob/a93dee7fd784993883374b211a021b706e80a433/LICENSE) | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `m5_with_covariates` | D | 30490 | 428849460 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [Subject to competition rules](https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules#7.-competition-data.) | [[2]](https://doi.org/10.1016/j.ijforecast.2021.07.007) |
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+ | `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | CC0: 1.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | CC BY 4.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |
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+ | `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | CC0: 1.0 | [[3]](https://doi.org/10.48550/arXiv.2307.07191) |