--- annotations_creators: - no-annotation license: other source_datasets: - original task_categories: - time-series-forecasting task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting dataset_info: - config_name: epf_electricity_be features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: Generation forecast sequence: float64 - name: System load forecast sequence: float64 splits: - name: train num_bytes: 1677334 num_examples: 1 download_size: 1001070 dataset_size: 1677334 - config_name: epf_electricity_de features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: Ampirion Load Forecast sequence: float64 - name: PV+Wind Forecast sequence: float64 splits: - name: train num_bytes: 1677334 num_examples: 1 download_size: 1285249 dataset_size: 1677334 - config_name: epf_electricity_fr features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: Generation forecast sequence: float64 - name: System load forecast sequence: float64 splits: - name: train num_bytes: 1677334 num_examples: 1 download_size: 1075381 dataset_size: 1677334 - config_name: epf_electricity_np features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: Grid load forecast sequence: float64 - name: Wind power forecast sequence: float64 splits: - name: train num_bytes: 1677334 num_examples: 1 download_size: 902996 dataset_size: 1677334 - config_name: epf_electricity_pjm features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: System load forecast sequence: float64 - name: Zonal COMED load foecast sequence: float64 splits: - name: train num_bytes: 1677335 num_examples: 1 download_size: 1396603 dataset_size: 1677335 - config_name: favorita_store_sales features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: sales sequence: float64 - name: onpromotion sequence: int64 - name: oil_price sequence: float64 - name: holiday sequence: string - name: store_nbr dtype: int64 - name: family dtype: string - name: city dtype: string - name: state dtype: string - name: type dtype: string - name: cluster dtype: int64 splits: - name: train num_bytes: 113609820 num_examples: 1782 download_size: 8385672 dataset_size: 113609820 - config_name: favorita_transactions features: - name: id dtype: int64 - name: timestamp sequence: timestamp[us] - name: transactions sequence: int64 - name: oil_price sequence: float64 - name: holiday sequence: string - name: store_nbr dtype: int64 - name: city dtype: string - name: state dtype: string - name: type dtype: string - name: cluster dtype: int64 splits: - name: train num_bytes: 2711975 num_examples: 54 download_size: 207866 dataset_size: 2711975 - config_name: m5_with_covariates features: - name: id dtype: string - name: timestamp sequence: timestamp[us] - name: target sequence: float64 - name: snap_CA sequence: int64 - name: snap_TX sequence: int64 - name: snap_WI sequence: int64 - name: sell_price sequence: float64 - name: event_Cultural sequence: int64 - name: event_National sequence: int64 - name: event_Religious sequence: int64 - name: event_Sporting sequence: int64 - name: item_id dtype: string - name: dept_id dtype: string - name: cat_id dtype: string - name: store_id dtype: string - name: state_id dtype: string splits: - name: train num_bytes: 3815531330 num_examples: 30490 download_size: 81672751 dataset_size: 3815531330 - config_name: proenfo_bull features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: float64 - name: dewtemperature sequence: float64 - name: sealvlpressure sequence: float64 splits: - name: train num_bytes: 28773967 num_examples: 41 download_size: 3893651 dataset_size: 28773967 - config_name: proenfo_cockatoo features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: float64 - name: dewtemperature sequence: float64 - name: sealvlpressure sequence: float64 - name: winddirection sequence: float64 - name: windspeed sequence: float64 splits: - name: train num_bytes: 982517 num_examples: 1 download_size: 408973 dataset_size: 982517 - config_name: proenfo_covid19 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: pressure_kpa sequence: float64 - name: cloud_cover_perc sequence: float64 - name: humidity_perc sequence: float64 - name: airtemperature sequence: float64 - name: wind_direction_deg sequence: float64 - name: wind_speed_kmh sequence: float64 splits: - name: train num_bytes: 2042408 num_examples: 1 download_size: 965912 dataset_size: 2042408 - config_name: proenfo_gfc12_load features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: float64 splits: - name: train num_bytes: 10405494 num_examples: 11 download_size: 3161406 dataset_size: 10405494 - config_name: proenfo_gfc14_load features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: float64 splits: - name: train num_bytes: 420500 num_examples: 1 download_size: 200463 dataset_size: 420500 - config_name: proenfo_gfc17_load features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: int64 splits: - name: train num_bytes: 3368608 num_examples: 8 download_size: 1562067 dataset_size: 3368608 - config_name: proenfo_hog features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: float64 - name: dewtemperature sequence: float64 - name: sealvlpressure sequence: float64 - name: winddirection sequence: float64 - name: windspeed sequence: float64 splits: - name: train num_bytes: 23580325 num_examples: 24 download_size: 3291179 dataset_size: 23580325 - config_name: proenfo_pdb features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: airtemperature sequence: int64 splits: - name: train num_bytes: 420500 num_examples: 1 download_size: 226285 dataset_size: 420500 - config_name: proenfo_spain features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: generation_biomass sequence: float64 - name: generation_fossil_brown_coal_lignite sequence: float64 - name: generation_fossil_coal_derived_gas sequence: float64 - name: generation_fossil_gas sequence: float64 - name: generation_fossil_hard_coal sequence: float64 - name: generation_fossil_oil sequence: float64 - name: generation_fossil_oil_shale sequence: float64 - name: generation_fossil_peat sequence: float64 - name: generation_geothermal sequence: float64 - name: generation_hydro_pumped_storage_consumption sequence: float64 - name: generation_hydro_run_of_river_and_poundage sequence: float64 - name: generation_hydro_water_reservoir sequence: float64 - name: generation_marine sequence: float64 - name: generation_nuclear sequence: float64 - name: generation_other sequence: float64 - name: generation_other_renewable sequence: float64 - name: generation_solar sequence: float64 - name: generation_waste sequence: float64 - name: generation_wind_offshore sequence: float64 - name: generation_wind_onshore sequence: float64 splits: - name: train num_bytes: 6171357 num_examples: 1 download_size: 1275626 dataset_size: 6171357 configs: - config_name: epf_electricity_be data_files: - split: train path: epf/electricity_be/train-* - config_name: epf_electricity_de data_files: - split: train path: epf/electricity_de/train-* - config_name: epf_electricity_fr data_files: - split: train path: epf/electricity_fr/train-* - config_name: epf_electricity_np data_files: - split: train path: epf/electricity_np/train-* - config_name: epf_electricity_pjm data_files: - split: train path: epf/electricity_pjm/train-* - config_name: favorita_store_sales data_files: - split: train path: favorita/store_sales/train-* - config_name: favorita_transactions data_files: - split: train path: favorita/transactions/train-* - config_name: m5_with_covariates data_files: - split: train path: m5_with_covariates/train-* - config_name: proenfo_bull data_files: - split: train path: proenfo/bull/train-* - config_name: proenfo_cockatoo data_files: - split: train path: proenfo/cockatoo/train-* - config_name: proenfo_covid19 data_files: - split: train path: proenfo/covid19/train-* - config_name: proenfo_gfc12_load data_files: - split: train path: proenfo/gfc12_load/train-* - config_name: proenfo_gfc14_load data_files: - split: train path: proenfo/gfc14_load/train-* - config_name: proenfo_gfc17_load data_files: - split: train path: proenfo/gfc17_load/train-* - config_name: proenfo_hog data_files: - split: train path: proenfo/hog/train-* - config_name: proenfo_pdb data_files: - split: train path: proenfo/pdb/train-* - config_name: proenfo_spain data_files: - split: train path: proenfo/spain/train-* --- ## Forecast evaluation datasets This repository contains time series datasets that can be used for evaluation of univariate & multivariate forecasting models. The main focus of this repository is on datasets that reflect real-world forecasting scenarios, such as those involving covariates, missing values, and other practical complexities. The datasets follow a format that is compatible with the [`fev`](https://github.com/autogluon/fev) package. ## Data format and usage Each dataset satisfies the following schema: - each dataset entry (=row) represents a single univariate or multivariate time series - each entry contains - 1/ a field of type `Sequence(timestamp)` that contains the timestamps of observations - 2/ at least one field of type `Sequence(float)` that can be used as the target time series or dynamic covariates - 3/ a field of type `string` that contains the unique ID of each time series - all fields of type `Sequence` have the same length Datasets can be loaded using the [🤗 `datasets`](https://huggingface.co/docs/datasets/en/index) library. ```python import datasets ds = datasets.load_dataset("autogluon/fev_datasets", "epf_electricity_de", split="train") ds.set_format("numpy") # sequences returned as numpy arrays ``` Example entry in the `epf_electricity_de` dataset ```python >>> ds[0] {'id': 'DE', 'timestamp': array(['2012-01-09T00:00:00.000000', '2012-01-09T01:00:00.000000', '2012-01-09T02:00:00.000000', ..., '2017-12-31T21:00:00.000000', '2017-12-31T22:00:00.000000', '2017-12-31T23:00:00.000000'], dtype='datetime64[us]'), 'target': array([34.97, 33.43, 32.74, ..., 5.3 , 1.86, -0.92], dtype=float32), 'Ampirion Load Forecast': array([16382. , 15410.5, 15595. , ..., 15715. , 15876. , 15130. ], dtype=float32), 'PV+Wind Forecast': array([ 3569.5276, 3315.275 , 3107.3076, ..., 29653.008 , 29520.33 , 29466.408 ], dtype=float32)} ``` 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). ## Dataset statistics **Disclaimer:** These datasets have been converted into a unified format from external sources. Please refer to the original sources for licensing and citation terms. We do not claim any rights to the original data. | config | freq | # items | # obs | # dynamic cols | # static cols | source | citation | |:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `favorita_store_sales` | D | 1782 | 12032064 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[2]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) | | `favorita_transactions` | D | 54 | 273456 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[2]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) | | `m5_with_covariates` | D | 30490 | 428849460 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[3]](https://doi.org/10.1016/j.ijforecast.2021.07.007) | | `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) | ## Publications using these datasets - ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107)