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Datasets:
autogluon
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fev_datasets

Tasks:
Time Series Forecasting
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Sub-tasks:
univariate-time-series-forecasting
multivariate-time-series-forecasting
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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
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 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_electricity_de", split="train")
ds.set_format("numpy")  # sequences returned as numpy arrays

Example entry in the epf_electricity_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 # obs # dynamic cols # static cols source citation
ETTh h 2 243880 7 0 https://github.com/zhouhaoyi/ETDataset [1]
ETTm 15min 2 975520 7 0 https://github.com/zhouhaoyi/ETDataset [1]
epf_electricity_be h 1 157248 3 0 https://zenodo.org/records/4624805 [2]
epf_electricity_de h 1 157248 3 0 https://zenodo.org/records/4624805 [2]
epf_electricity_fr h 1 157248 3 0 https://zenodo.org/records/4624805 [2]
epf_electricity_np h 1 157248 3 0 https://zenodo.org/records/4624805 [2]
epf_electricity_pjm h 1 157248 3 0 https://zenodo.org/records/4624805 [2]
favorita_store_sales D 1782 12032064 4 6 https://www.kaggle.com/competitions/store-sales-time-series-forecasting [3]
favorita_transactions D 54 273456 3 5 https://www.kaggle.com/competitions/store-sales-time-series-forecasting [3]
m5_with_covariates D 30490 428849460 9 5 https://www.kaggle.com/competitions/m5-forecasting-accuracy [4]
proenfo_bull h 41 2877216 4 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_cockatoo h 1 105264 6 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_covid19 h 1 223384 7 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_gfc12_load h 11 867108 2 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_gfc14_load h 1 35040 2 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_gfc17_load h 8 280704 2 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_hog h 24 2526336 6 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_pdb h 1 35040 2 0 https://github.com/Leo-VK/EnFoAV [5]
proenfo_spain h 1 736344 21 0 https://github.com/Leo-VK/EnFoAV [5]

Publications using these datasets