autogluon/fev_datasets

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

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