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ETTm1
["2016-07-01T00:00:00","2016-07-01T00:15:00","2016-07-01T00:30:00","2016-07-01T00:45:00","2016-07-01(...TRUNCATED)
[5.827000141143799,5.760000228881836,5.760000228881836,5.760000228881836,5.692999839782715,5.4920001(...TRUNCATED)
[2.009000062942505,2.0759999752044678,1.9420000314712524,1.9420000314712524,2.0759999752044678,1.942(...TRUNCATED)
[1.5989999771118164,1.4919999837875366,1.4919999837875366,1.4919999837875366,1.4919999837875366,1.45(...TRUNCATED)
[0.4620000123977661,0.4259999990463257,0.39100000262260437,0.4259999990463257,0.4259999990463257,0.3(...TRUNCATED)
[4.203000068664551,4.263999938964844,4.234000205993652,4.234000205993652,4.142000198364258,4.1119999(...TRUNCATED)
[1.340000033378601,1.4010000228881836,1.309999942779541,1.309999942779541,1.371000051498413,1.279000(...TRUNCATED)
[30.5310001373291,30.459999084472656,30.038000106811523,27.01300048828125,27.78700065612793,27.71699(...TRUNCATED)
ETTm2
["2016-07-01T00:00:00","2016-07-01T00:15:00","2016-07-01T00:30:00","2016-07-01T00:45:00","2016-07-01(...TRUNCATED)
[41.130001068115234,39.62200164794922,38.86800003051758,35.518001556396484,37.52799987792969,37.6119(...TRUNCATED)
[12.480999946594238,11.309000015258789,10.555000305175781,9.21399974822998,10.13599967956543,10.3030(...TRUNCATED)
[36.5359992980957,35.54399871826172,34.3650016784668,32.569000244140625,33.93600082397461,33.2120018(...TRUNCATED)
[9.354999542236328,8.550999641418457,7.585999965667725,8.711999893188477,7.5320000648498535,7.532000(...TRUNCATED)
[4.423999786376953,3.2090001106262207,4.434999942779541,4.434999942779541,4.434999942779541,3.913000(...TRUNCATED)
[1.3109999895095825,1.2580000162124634,1.2580000162124634,1.215000033378601,1.215000033378601,1.3009(...TRUNCATED)
[38.6619987487793,38.222999572753906,37.34400177001953,37.124000549316406,37.124000549316406,36.9039(...TRUNCATED)

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]
beijing_air_quality h 12 35,064 4,628,448 11 0 https://huggingface.co/datasets/Salesforce/lotsa_data [3]
bizitobs_l2c h 1 2,664 18,648 7 0 https://huggingface.co/datasets/Salesforce/GiftEval [4]
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]
cdc_fluview_ilinet W-SUN 75 680 319,515 5 0 https://huggingface.co/datasets/Salesforce/lotsa_data [3]
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_2018_1D D 270 455 122,791 1 0 https://huggingface.co/datasets/Salesforce/GiftEval [4]
kdd_cup_2018_1H h 270 10,898 2,942,364 1 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_10T 10min 137 52,560 7,200,720 1 0 https://huggingface.co/datasets/Salesforce/GiftEval [4]
solar_1D D 137 365 50,005 1 0 https://huggingface.co/datasets/Salesforce/GiftEval [4]
solar_1H h 137 8,760 1,200,120 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
uci_air_quality h 1 9,357 121,641 13 0 https://archive.ics.uci.edu/dataset/360/air+quality [20]
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

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