| --- |
| 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. |
|
|
|
|
| | 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) |
