README.md

metadata

annotations_creators:
  - no-annotation
license: other
source_datasets:
  - original
task_categories:
  - time-series-forecasting
task_ids:
  - univariate-time-series-forecasting
  - multivariate-time-series-forecasting
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        sequence: timestamp[ms]
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  - config_name: proenfo_pdb
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  - config_name: redset_1H
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        sequence: timestamp[ms]
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  - config_name: redset_5T
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  - config_name: restaurant
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  - config_name: rohlik_sales_1D
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  - config_name: uk_covid_nation_1D
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  - config_name: walmart
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  - config_name: world_life_expectancy
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  - config_name: world_tourism
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configs:
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        path: ETT/1W/train-*
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  - config_name: M_DENSE_1D
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        path: boomlet/963/train-*
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        path: ecdc_ili/train-*
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        path: favorita_stores/1W/train-*
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    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_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-*
  - config_name: proenfo_gfc12
    data_files:
      - split: train
        path: proenfo_gfc12/train-*
  - config_name: proenfo_gfc14
    data_files:
      - split: train
        path: proenfo_gfc14/train-*
  - config_name: proenfo_gfc17
    data_files:
      - split: train
        path: proenfo_gfc17/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: redset_15T
    data_files:
      - split: train
        path: redset/15T/train-*
  - config_name: redset_1H
    data_files:
      - split: train
        path: redset/1H/train-*
  - config_name: redset_5T
    data_files:
      - split: train
        path: redset/5T/train-*
  - config_name: restaurant
    data_files:
      - split: train
        path: restaurant/train-*
  - config_name: rohlik_orders_1D
    data_files:
      - split: train
        path: rohlik_orders/1D/train-*
  - config_name: rohlik_orders_1W
    data_files:
      - split: train
        path: rohlik_orders/1W/train-*
  - config_name: rohlik_sales_1D
    data_files:
      - split: train
        path: rohlik_sales/1D/train-*
  - config_name: rohlik_sales_1W
    data_files:
      - split: train
        path: rohlik_sales/1W/train-*
  - config_name: rossmann_1D
    data_files:
      - split: train
        path: rossmann/1D/train-*
  - config_name: rossmann_1W
    data_files:
      - split: train
        path: rossmann/1W/train-*
  - config_name: solar_1D
    data_files:
      - split: train
        path: solar/1D/train-*
  - config_name: solar_1W
    data_files:
      - split: train
        path: solar/1W/train-*
  - config_name: solar_with_weather_15T
    data_files:
      - split: train
        path: solar_with_weather/15T/train-*
  - config_name: solar_with_weather_1H
    data_files:
      - split: train
        path: solar_with_weather/1H/train-*
  - config_name: uk_covid_nation_1D
    data_files:
      - split: train
        path: uk_covid_nation/1D/train-*
  - config_name: uk_covid_nation_1W
    data_files:
      - split: train
        path: uk_covid_nation/1W/train-*
  - config_name: uk_covid_utla_1D
    data_files:
      - split: train
        path: uk_covid_utla/1D/train-*
  - config_name: uk_covid_utla_1W
    data_files:
      - split: train
        path: uk_covid_utla/1W/train-*
  - config_name: us_consumption_1M
    data_files:
      - split: train
        path: us_consumption/1M/train-*
  - config_name: us_consumption_1Q
    data_files:
      - split: train
        path: us_consumption/1Q/train-*
  - config_name: us_consumption_1Y
    data_files:
      - split: train
        path: us_consumption/1Y/train-*
  - config_name: walmart
    data_files:
      - split: train
        path: walmart/train-*
  - config_name: world_co2_emissions
    data_files:
      - split: train
        path: world_co2_emissions/train-*
  - config_name: world_life_expectancy
    data_files:
      - split: train
        path: world_life_expectancy/train-*
  - config_name: world_tourism
    data_files:
      - split: train
        path: world_tourism/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 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 library.

import datasets

ds = datasets.load_dataset("autogluon/fev_datasets", "epf_de", split="train")
ds.set_format("numpy")  # sequences returned as numpy arrays

Example entry in the epf_de dataset

>>> 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.

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	median length	# obs	# dynamic cols	# static cols	source	citation
ETT_15T	15min	2	69,680	975,520	7	0	https://github.com/zhouhaoyi/ETDataset	[1]
ETT_1H	h	2	17,420	243,880	7	0	https://github.com/zhouhaoyi/ETDataset	[1]
LOOP_SEATTLE_1D	D	323	365	117,895	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
LOOP_SEATTLE_1H	h	323	8,760	2,829,480	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
LOOP_SEATTLE_5T	5min	323	105,120	33,953,760	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
M_DENSE_1D	D	30	730	21,900	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
M_DENSE_1H	h	30	17,520	525,600	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
SZ_TAXI_15T	15min	156	2,976	464,256	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
SZ_TAXI_1H	h	156	744	116,064	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[2]
boomlet_1062	5min	1	16,384	344,064	21	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1209	5min	1	16,384	868,352	53	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1225	min	1	16,384	802,816	49	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1230	5min	1	16,384	376,832	23	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1282	min	1	16,384	573,440	35	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1487	5min	1	16,384	884,736	54	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1631	30min	1	10,463	418,520	40	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1676	30min	1	10,463	1,046,300	100	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1855	h	1	5,231	272,012	52	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_1975	h	1	5,231	392,325	75	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_2187	h	1	5,231	523,100	100	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_285	min	1	16,384	1,228,800	75	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_619	min	1	16,384	851,968	52	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_772	min	1	16,384	1,097,728	67	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
boomlet_963	min	1	16,384	458,752	28	6	https://huggingface.co/datasets/Datadog/BOOM	[5]
ecdc_ili	W-SUN	25	201	4,797	1	0	https://github.com/EU-ECDC/Respiratory_viruses_weekly_data/blob/main/data/snapshots/2025-08-08_ILIARIRates.csv
epf_be	h	1	52,416	157,248	3	0	https://zenodo.org/records/4624805	[6]
epf_de	h	1	52,416	157,248	3	0	https://zenodo.org/records/4624805	[6]
epf_fr	h	1	52,416	157,248	3	0	https://zenodo.org/records/4624805	[6]
epf_np	h	1	52,416	157,248	3	0	https://zenodo.org/records/4624805	[6]
epf_pjm	h	1	52,416	157,248	3	0	https://zenodo.org/records/4624805	[6]
ercot_1D	D	8	6,452	51,616	1	0	https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy
ercot_1H	h	8	154,872	1,238,976	1	0	https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy
ercot_1M	ME	8	211	1,688	1	0	https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy
ercot_1W	W-SUN	8	921	7,368	1	0	https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy
favorita_stores_1D	D	1,579	1,688	10,661,408	4	6	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
favorita_stores_1M	ME	1,579	54	255,798	3	6	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
favorita_stores_1W	W-SUN	1,579	240	1,136,880	3	6	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
favorita_transactions_1D	D	51	1,688	258,264	3	5	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
favorita_transactions_1M	ME	51	54	5,508	2	5	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
favorita_transactions_1W	W-SUN	51	240	24,480	2	5	https://www.kaggle.com/competitions/store-sales-time-series-forecasting	[7]
fred_md_2025	MS	1	798	100,548	126	0	https://www.stlouisfed.org/research/economists/mccracken/fred-databases	[8]
fred_qd_2025	QS-DEC	1	266	65,170	245	0	https://www.stlouisfed.org/research/economists/mccracken/fred-databases	[9]
gvar	QS-OCT	33	178	52,866	9	0	https://data.mendeley.com/datasets/kfp5fhgkvf/1	[10]
hermes	W-MON	10,000	261	5,220,000	2	2	https://github.com/etidav/HERMES	[11]
hierarchical_sales_1D	D	118	1,825	215,350	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
hierarchical_sales_1W	W-WED	118	260	30,680	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
hierarchical_tourism	QE-DEC	89	36	3,204	1	0	https://robjhyndman.com/publications/hierarchical-tourism/	[12]
hospital_admissions_1D	D	8	1,731	13,846	1	0	https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024	[13]
hospital_admissions_1W	W-SUN	8	246	1,968	1	0	https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024	[13]
hospital	ME	767	84	64,428	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
jena_weather_10T	10min	1	52,704	1,106,784	21	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
jena_weather_1D	D	1	366	7,686	21	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
jena_weather_1H	h	1	8,784	184,464	21	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
kdd_cup_2022_10T	10min	134	35,279	47,273,860	10	0	https://aistudio.baidu.com/competition/detail/152/0/task-definition	[14]
kdd_cup_2022_1D	D	134	243	325,620	10	0	https://aistudio.baidu.com/competition/detail/152/0/task-definition	[14]
kdd_cup_2022_30T	30min	134	11,758	15,755,720	10	0	https://aistudio.baidu.com/competition/detail/152/0/task-definition	[14]
m5_1D	D	30,490	1,810	428,849,460	9	5	https://www.kaggle.com/competitions/m5-forecasting-accuracy	[15]
m5_1M	ME	30,490	58	13,805,685	9	5	https://www.kaggle.com/competitions/m5-forecasting-accuracy	[15]
m5_1W	W-SUN	30,490	257	60,857,703	9	5	https://www.kaggle.com/competitions/m5-forecasting-accuracy	[15]
proenfo_bull	h	41	17,544	2,877,216	4	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_cockatoo	h	1	17,544	105,264	6	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_gfc12	h	11	39,414	867,108	2	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_gfc14	h	1	17,520	35,040	2	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_gfc17	h	8	17,544	280,704	2	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_hog	h	24	17,544	2,526,336	6	0	https://github.com/Leo-VK/EnFoAV	[16]
proenfo_pdb	h	1	17,520	35,040	2	0	https://github.com/Leo-VK/EnFoAV	[16]
redset_15T	15min	126	8,640	1,052,371	1	1	https://github.com/amazon-science/redset/	[17]
redset_1H	h	138	2,160	283,070	1	1	https://github.com/amazon-science/redset/	[17]
redset_5T	5min	118	25,920	2,960,408	1	1	https://github.com/amazon-science/redset/	[17]
restaurant	D	817	296	294,568	1	4	https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting	[18]
rossmann_1D	D	1,115	942	7,352,310	7	10	https://www.kaggle.com/competitions/rossmann-store-sales	[19]
rossmann_1W	W-SUN	1,115	133	889,770	6	10	https://www.kaggle.com/competitions/rossmann-store-sales	[19]
solar_1D	D	137	365	50,005	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
solar_1W	W-FRI	137	52	7,124	1	0	https://huggingface.co/datasets/Salesforce/GiftEval	[4]
solar_with_weather_15T	15min	1	198,600	1,986,000	10	0	https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions
solar_with_weather_1H	h	1	49,648	496,480	10	0	https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions
uk_covid_nation_1D	D	4	729	41,216	14	0	https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed
uk_covid_nation_1W	W-SUN	4	105	5,936	14	0	https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed
uk_covid_utla_1D	D	214	721	308,786	2	0	https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed
uk_covid_utla_1W	W-SUN	214	104	44,448	2	0	https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed
us_consumption_1M	MS	31	792	24,552	1	0	https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying	[21]
us_consumption_1Q	QE-DEC	31	262	8,122	1	0	https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying	[21]
us_consumption_1Y	YE-DEC	31	64	1,984	1	0	https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying	[21]
walmart	W-FRI	2,936	143	4,609,143	11	4	https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting	[22]
world_co2_emissions	YE-DEC	191	60	11,460	1	0	https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country
world_life_expectancy	YE-DEC	237	74	17,538	1	0	https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023	[23]
world_tourism	YE-DEC	178	21	3,738	1	0	https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact

Publications using these datasets

• "ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"