Datasets:
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README.md
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num_examples: 118
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download_size: 43113
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dataset_size: 370602
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features:
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sequence: float32
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splits:
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num_bytes: 40729
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num_examples: 89
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download_size: 16231
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dataset_size: 40729
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data_files:
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path: hierarchical_sales/1W/train-*
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- config_name: hierarchical_tourism
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data_files:
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path: hierarchical_tourism/train-*
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data_files:
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- split: train
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| `M_DENSE_1H` | h | 30 | 17,520 | 525,600 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `SZ_TAXI_15T` | 15min | 156 | 2,976 | 464,256 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `SZ_TAXI_1H` | h | 156 | 744 | 116,064 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `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 | |
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| `entsoe_15T` | 15min | 6 | 175,292 | 6,310,512 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[
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| `entsoe_1H` | h | 6 | 43,822 | 1,577,592 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[
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| `entsoe_30T` | 30min | 6 | 87,645 | 3,155,220 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[
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| `epf_be` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `epf_de` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `epf_fr` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `epf_np` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `epf_pjm` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `ercot_1D` | D | 8 | 6,452 | 51,616 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1H` | h | 8 | 154,872 | 1,238,976 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1M` | ME | 8 | 211 | 1,688 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1W` | W-SUN | 8 | 921 | 7,368 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `favorita_stores_1D` | D | 1,579 | 1,688 | 10,661,408 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_stores_1M` | ME | 1,579 | 54 | 255,798 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_stores_1W` | W-SUN | 1,579 | 240 | 1,136,880 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_transactions_1D` | D | 51 | 1,688 | 258,264 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_transactions_1M` | ME | 51 | 54 | 5,508 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_transactions_1W` | W-SUN | 51 | 240 | 24,480 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `fred_md_2025` | MS | 1 | 798 | 100,548 | 126 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[
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| `fred_qd_2025` | QS-DEC | 1 | 266 | 65,170 | 245 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[
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| `gvar` | QS-OCT | 33 | 178 | 52,866 | 9 | 0 | https://data.mendeley.com/datasets/kfp5fhgkvf/1 | [[
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| `hermes` | W-MON | 10,000 | 261 | 5,220,000 | 2 | 2 | https://github.com/etidav/HERMES | [[
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| `hierarchical_sales_1D` | D | 118 | 1,825 | 215,350 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `hierarchical_sales_1W` | W-WED | 118 | 260 | 30,680 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `hierarchical_tourism` | QE-DEC | 89 | 36 | 3,204 | 1 | 0 | https://robjhyndman.com/publications/hierarchical-tourism/ | [[12]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
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| `hospital_admissions_1D` | D | 8 | 1,731 | 13,846 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
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| `hospital_admissions_1W` | W-SUN | 8 | 246 | 1,968 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
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| `hospital` | ME | 767 | 84 | 64,428 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `jena_weather_10T` | 10min | 1 | 52,704 | 1,106,784 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `jena_weather_1D` | D | 1 | 366 | 7,686 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `jena_weather_1H` | h | 1 | 8,784 | 184,464 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `kdd_cup_2022_10T` | 10min | 134 | 35,279 | 47,273,860 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
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| `kdd_cup_2022_1D` | D | 134 | 243 | 325,620 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
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| `kdd_cup_2022_30T` | 30min | 134 | 11,758 | 15,755,720 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
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| `rohlik_sales_1W` | W-SUN | 5,243 | 150 | 10,516,770 | 15 | 7 | https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2 | [[20]](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2/overview/citation) |
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| `rossmann_1D` | D | 1,115 | 942 | 7,352,310 | 7 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[21]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
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| `rossmann_1W` | W-SUN | 1,115 | 133 | 889,770 | 6 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[21]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
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| `solar_1D` | D | 137 | 365 | 50,005 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `solar_1W` | W-FRI | 137 | 52 | 7,124 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[
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| `solar_with_weather_15T` | 15min | 1 | 198,600 | 1,986,000 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
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| `solar_with_weather_1H` | h | 1 | 49,648 | 496,480 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
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| `uci_air_quality_1D` | D | 1 | 389 | 5,057 | 13 | 0 | https://archive.ics.uci.edu/dataset/360/air+quality | [[22]](https://doi.org/10.24432/C59K5F) |
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| `world_life_expectancy` | YE-DEC | 237 | 74 | 17,538 | 1 | 0 | https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023 | [[25]](https://ourworldindata.org/life-expectancy#article-citation) |
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| `world_tourism` | YE-DEC | 178 | 21 | 3,738 | 1 | 0 | https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact | [[26]](https://www.worldbank.org/en/archive/using-the-archives/terms-of-use-reproduction-and-citation) |
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## Publications using these datasets
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- ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107)
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num_examples: 118
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download_size: 43113
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dataset_size: 370602
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- config_name: hospital
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data_files:
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- split: train
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path: hierarchical_sales/1W/train-*
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- config_name: hospital
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data_files:
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- split: train
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| `M_DENSE_1H` | h | 30 | 17,520 | 525,600 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `SZ_TAXI_15T` | 15min | 156 | 2,976 | 464,256 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `SZ_TAXI_1H` | h | 156 | 744 | 116,064 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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| `australian_tourism` | QE-DEC | 89 | 36 | 3,204 | 1 | 0 | https://robjhyndman.com/publications/hierarchical-tourism/ | [[3]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
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| `bizitobs_l2c_1H` | h | 1 | 2,664 | 18,648 | 7 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
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| `bizitobs_l2c_5T` | 5min | 1 | 31,968 | 223,776 | 7 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
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| `boomlet_1062` | 5min | 1 | 16,384 | 344,064 | 21 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1209` | 5min | 1 | 16,384 | 868,352 | 53 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1225` | min | 1 | 16,384 | 802,816 | 49 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1230` | 5min | 1 | 16,384 | 376,832 | 23 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1282` | min | 1 | 16,384 | 573,440 | 35 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1487` | 5min | 1 | 16,384 | 884,736 | 54 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1631` | 30min | 1 | 10,463 | 418,520 | 40 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1676` | 30min | 1 | 10,463 | 1,046,300 | 100 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1855` | h | 1 | 5,231 | 272,012 | 52 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_1975` | h | 1 | 5,231 | 392,325 | 75 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_2187` | h | 1 | 5,231 | 523,100 | 100 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_285` | min | 1 | 16,384 | 1,228,800 | 75 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_619` | min | 1 | 16,384 | 851,968 | 52 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_772` | min | 1 | 16,384 | 1,097,728 | 67 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `boomlet_963` | min | 1 | 16,384 | 458,752 | 28 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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| `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 | |
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| `entsoe_15T` | 15min | 6 | 175,292 | 6,310,512 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
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| `entsoe_1H` | h | 6 | 43,822 | 1,577,592 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
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| `entsoe_30T` | 30min | 6 | 87,645 | 3,155,220 | 6 | 0 | https://data.open-power-system-data.org/time_series/2020-10-06 | [[6]](https://doi.org/10.25832/time_series/2020-10-06) |
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| `epf_be` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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| `epf_de` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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| `epf_fr` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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| `epf_np` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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| `epf_pjm` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[7]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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| `ercot_1D` | D | 8 | 6,452 | 51,616 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1H` | h | 8 | 154,872 | 1,238,976 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1M` | ME | 8 | 211 | 1,688 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `ercot_1W` | W-SUN | 8 | 921 | 7,368 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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| `favorita_stores_1D` | D | 1,579 | 1,688 | 10,661,408 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
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| `favorita_stores_1M` | ME | 1,579 | 54 | 255,798 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
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| `favorita_stores_1W` | W-SUN | 1,579 | 240 | 1,136,880 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
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| `favorita_transactions_1D` | D | 51 | 1,688 | 258,264 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
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| 5348 |
+
| `favorita_transactions_1M` | ME | 51 | 54 | 5,508 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
|
| 5349 |
+
| `favorita_transactions_1W` | W-SUN | 51 | 240 | 24,480 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[8]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
|
| 5350 |
+
| `fred_md_2025` | MS | 1 | 798 | 100,548 | 126 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[9]](https://doi.org/10.20955/wp.2015.012) |
|
| 5351 |
+
| `fred_qd_2025` | QS-DEC | 1 | 266 | 65,170 | 245 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[10]](https://doi.org/10.20955/wp.2020.005) |
|
| 5352 |
+
| `gvar` | QS-OCT | 33 | 178 | 52,866 | 9 | 0 | https://data.mendeley.com/datasets/kfp5fhgkvf/1 | [[11]](https://doi.org/10.17863/CAM.104755) |
|
| 5353 |
+
| `hermes` | W-MON | 10,000 | 261 | 5,220,000 | 2 | 2 | https://github.com/etidav/HERMES | [[12]](https://arxiv.org/abs/2202.03224) |
|
| 5354 |
+
| `hierarchical_sales_1D` | D | 118 | 1,825 | 215,350 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5355 |
+
| `hierarchical_sales_1W` | W-WED | 118 | 260 | 30,680 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
|
|
|
| 5356 |
| `hospital_admissions_1D` | D | 8 | 1,731 | 13,846 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
|
| 5357 |
| `hospital_admissions_1W` | W-SUN | 8 | 246 | 1,968 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
|
| 5358 |
+
| `hospital` | ME | 767 | 84 | 64,428 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5359 |
+
| `jena_weather_10T` | 10min | 1 | 52,704 | 1,106,784 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5360 |
+
| `jena_weather_1D` | D | 1 | 366 | 7,686 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5361 |
+
| `jena_weather_1H` | h | 1 | 8,784 | 184,464 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5362 |
| `kdd_cup_2022_10T` | 10min | 134 | 35,279 | 47,273,860 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
|
| 5363 |
| `kdd_cup_2022_1D` | D | 134 | 243 | 325,620 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
|
| 5364 |
| `kdd_cup_2022_30T` | 30min | 134 | 11,758 | 15,755,720 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
|
|
|
|
| 5382 |
| `rohlik_sales_1W` | W-SUN | 5,243 | 150 | 10,516,770 | 15 | 7 | https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2 | [[20]](https://www.kaggle.com/competitions/rohlik-sales-forecasting-challenge-v2/overview/citation) |
|
| 5383 |
| `rossmann_1D` | D | 1,115 | 942 | 7,352,310 | 7 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[21]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
|
| 5384 |
| `rossmann_1W` | W-SUN | 1,115 | 133 | 889,770 | 6 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[21]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
|
| 5385 |
+
| `solar_1D` | D | 137 | 365 | 50,005 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5386 |
+
| `solar_1W` | W-FRI | 137 | 52 | 7,124 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
|
| 5387 |
| `solar_with_weather_15T` | 15min | 1 | 198,600 | 1,986,000 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
|
| 5388 |
| `solar_with_weather_1H` | h | 1 | 49,648 | 496,480 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
|
| 5389 |
| `uci_air_quality_1D` | D | 1 | 389 | 5,057 | 13 | 0 | https://archive.ics.uci.edu/dataset/360/air+quality | [[22]](https://doi.org/10.24432/C59K5F) |
|
|
|
|
| 5400 |
| `world_life_expectancy` | YE-DEC | 237 | 74 | 17,538 | 1 | 0 | https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023 | [[25]](https://ourworldindata.org/life-expectancy#article-citation) |
|
| 5401 |
| `world_tourism` | YE-DEC | 178 | 21 | 3,738 | 1 | 0 | https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact | [[26]](https://www.worldbank.org/en/archive/using-the-archives/terms-of-use-reproduction-and-citation) |
|
| 5402 |
|
| 5403 |
+
## Citation
|
| 5404 |
+
If you find these datasets useful in your work, please cite the following [paper](https://arxiv.org/abs/2509.26468)
|
| 5405 |
+
```
|
| 5406 |
+
@article{shchur2025fev,
|
| 5407 |
+
title={{fev-bench}: A Realistic Benchmark for Time Series Forecasting},
|
| 5408 |
+
author={Shchur, Oleksandr and Ansari, Abdul Fatir and Turkmen, Caner and Stella, Lorenzo and Erickson, Nick and Guerron, Pablo and Bohlke-Schneider, Michael and Wang, Yuyang},
|
| 5409 |
+
year={2025},
|
| 5410 |
+
eprint={2509.26468},
|
| 5411 |
+
archivePrefix={arXiv},
|
| 5412 |
+
primaryClass={cs.LG}
|
| 5413 |
+
}
|
| 5414 |
+
```
|
| 5415 |
+
|
| 5416 |
## Publications using these datasets
|
| 5417 |
|
| 5418 |
- ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107)
|