Datasets:

autogluon
/

fev_datasets

	---
	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:
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	- config_name: SZ_TAXI_1H
	data_files:
	- split: train
	path: SZ_TAXI/1H/train-*
	- config_name: beijing_air_quality
	data_files:
	- split: train
	path: beijing_air_quality/train-*
	- config_name: bizitobs_l2c
	data_files:
	- split: train
	path: bizitobs_l2c/train-*
	- config_name: boomlet_1062
	data_files:
	- split: train
	path: boomlet/1062/train-*
	- config_name: boomlet_1209
	data_files:
	- split: train
	path: boomlet/1209/train-*
	- config_name: boomlet_1225
	data_files:
	- split: train
	path: boomlet/1225/train-*
	- config_name: boomlet_1230
	data_files:
	- split: train
	path: boomlet/1230/train-*
	- config_name: boomlet_1282
	data_files:
	- split: train
	path: boomlet/1282/train-*
	- config_name: boomlet_1487
	data_files:
	- split: train
	path: boomlet/1487/train-*
	- config_name: boomlet_1631
	data_files:
	- split: train
	path: boomlet/1631/train-*
	- config_name: boomlet_1676
	data_files:
	- split: train
	path: boomlet/1676/train-*
	- config_name: boomlet_1855
	data_files:
	- split: train
	path: boomlet/1855/train-*
	- config_name: boomlet_1975
	data_files:
	- split: train
	path: boomlet/1975/train-*
	- config_name: boomlet_2187
	data_files:
	- split: train
	path: boomlet/2187/train-*
	- config_name: boomlet_285
	data_files:
	- split: train
	path: boomlet/285/train-*
	- config_name: boomlet_619
	data_files:
	- split: train
	path: boomlet/619/train-*
	- config_name: boomlet_772
	data_files:
	- split: train
	path: boomlet/772/train-*
	- config_name: boomlet_963
	data_files:
	- split: train
	path: boomlet/963/train-*
	- config_name: cdc_fluview_ilinet
	data_files:
	- split: train
	path: cdc_fluview_ilinet/train-*
	- config_name: ecdc_ili
	data_files:
	- split: train
	path: ecdc_ili/train-*
	- config_name: epf_be
	data_files:
	- split: train
	path: epf_be/train-*
	- config_name: epf_de
	data_files:
	- split: train
	path: epf_de/train-*
	- config_name: epf_fr
	data_files:
	- split: train
	path: epf_fr/train-*
	- config_name: epf_np
	data_files:
	- split: train
	path: epf_np/train-*
	- config_name: epf_pjm
	data_files:
	- split: train
	path: epf_pjm/train-*
	- config_name: ercot_1D
	data_files:
	- split: train
	path: ercot/1D/train-*
	- config_name: ercot_1H
	data_files:
	- split: train
	path: ercot/1H/train-*
	- config_name: ercot_1M
	data_files:
	- split: train
	path: ercot/1M/train-*
	- config_name: ercot_1W
	data_files:
	- split: train
	path: ercot/1W/train-*
	- config_name: favorita_stores_1D
	data_files:
	- split: train
	path: favorita_stores/1D/train-*
	- config_name: favorita_stores_1M
	data_files:
	- split: train
	path: favorita_stores/1M/train-*
	- config_name: favorita_stores_1W
	data_files:
	- split: train
	path: favorita_stores/1W/train-*
	- config_name: favorita_transactions_1D
	data_files:
	- split: train
	path: favorita_transactions/1D/train-*
	- config_name: favorita_transactions_1M
	data_files:
	- split: train
	path: favorita_transactions/1M/train-*
	- config_name: favorita_transactions_1W
	data_files:
	- split: train
	path: favorita_transactions/1W/train-*
	- config_name: fred_md_2025
	data_files:
	- split: train
	path: fred_md_2025/train-*
	- config_name: fred_qd_2025
	data_files:
	- split: train
	path: fred_qd_2025/train-*
	- config_name: gvar
	data_files:
	- split: train
	path: gvar/train-*
	- config_name: hermes
	data_files:
	- split: train
	path: hermes/train-*
	- config_name: hierarchical_sales_1D
	data_files:
	- split: train
	path: hierarchical_sales/1D/train-*
	- config_name: hierarchical_sales_1W
	data_files:
	- split: train
	path: hierarchical_sales/1W/train-*
	- config_name: hierarchical_tourism
	data_files:
	- split: train
	path: hierarchical_tourism/train-*
	- config_name: hospital
	data_files:
	- split: train
	path: hospital/train-*
	- config_name: hospital_admissions_1D
	data_files:
	- split: train
	path: hospital_admissions/1D/train-*
	- config_name: hospital_admissions_1W
	data_files:
	- split: train
	path: hospital_admissions/1W/train-*
	- config_name: jena_weather_10T
	data_files:
	- split: train
	path: jena_weather/10T/train-*
	- config_name: jena_weather_1D
	data_files:
	- split: train
	path: jena_weather/1D/train-*
	- config_name: jena_weather_1H
	data_files:
	- split: train
	path: jena_weather/1H/train-*
	- config_name: kdd_cup_2018_1D
	data_files:
	- split: train
	path: kdd_cup_2018/1D/train-*
	- config_name: kdd_cup_2018_1H
	data_files:
	- split: train
	path: kdd_cup_2018/1H/train-*
	- config_name: kdd_cup_2022_10T
	data_files:
	- split: train
	path: kdd_cup_2022/10T/train-*
	- config_name: kdd_cup_2022_1D
	data_files:
	- split: train
	path: kdd_cup_2022/1D/train-*
	- config_name: kdd_cup_2022_30T
	data_files:
	- split: train
	path: kdd_cup_2022/30T/train-*
	- config_name: m5_1D
	data_files:
	- split: train
	path: m5/1D/train-*
	- config_name: m5_1M
	data_files:
	- split: train
	path: m5/1M/train-*
	- config_name: m5_1W
	data_files:
	- split: train
	path: m5/1W/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-*
	---

	## 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)