--- 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: ETTh features: - name: id dtype: string - name: timestamp sequence: timestamp[ns] - name: HUFL sequence: float64 - name: HULL sequence: float64 - name: MUFL sequence: float64 - name: MULL sequence: float64 - name: LUFL sequence: float64 - name: LULL sequence: float64 - name: OT sequence: float64 splits: - name: train num_bytes: 2229842 num_examples: 2 download_size: 569100 dataset_size: 2229842 - config_name: ETTm features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: HUFL sequence: float64 - name: HULL sequence: float64 - name: MUFL sequence: float64 - name: MULL sequence: float64 - name: LUFL sequence: float64 - name: LULL sequence: float64 - name: OT sequence: float64 splits: - name: train num_bytes: 8919122 num_examples: 2 download_size: 1986490 dataset_size: 8919122 - 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: ETTh data_files: - split: train path: ETTh/train-* - config_name: ETTm data_files: - split: train path: ETTm/train-* - 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. Unless otherwise specified, the datasets are provided only for research purposes. | config | freq | # items | # obs | # dynamic cols | # static cols | source | citation | |:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | `ETTh` | h | 2 | 243880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | | `ETTm` | 15min | 2 | 975520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | | `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) | | `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](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 | [[3]](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 | [[3]](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 | [[4]](https://doi.org/10.1016/j.ijforecast.2021.07.007) | | `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) | | `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](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)