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
dataset_info:
  - 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: 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.

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

Publications using these datasets

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