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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: ETT_15T
    features:
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
      - name: HUFL
        sequence: float32
      - name: HULL
        sequence: float32
      - name: MUFL
        sequence: float32
      - name: MULL
        sequence: float32
      - name: LUFL
        sequence: float32
      - name: LULL
        sequence: float32
      - name: OT
        sequence: float32
    splits:
      - name: train
        num_bytes: 5017042
        num_examples: 2
    download_size: 1964373
    dataset_size: 5017042
  - config_name: ETT_1H
    features:
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
      - name: HUFL
        sequence: float32
      - name: HULL
        sequence: float32
      - name: MUFL
        sequence: float32
      - name: MULL
        sequence: float32
      - name: LUFL
        sequence: float32
      - name: LULL
        sequence: float32
      - name: OT
        sequence: float32
    splits:
      - name: train
        num_bytes: 1254322
        num_examples: 2
    download_size: 531145
    dataset_size: 1254322
  - 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: LOOP_SEATTLE_1D
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 1419475
        num_examples: 323
    download_size: 750221
    dataset_size: 1419475
  - config_name: LOOP_SEATTLE_1H
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 33958495
        num_examples: 323
    download_size: 16373920
    dataset_size: 33958495
  - config_name: LOOP_SEATTLE_5T
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 407449855
        num_examples: 323
    download_size: 209147833
    dataset_size: 407449855
  - config_name: M_DENSE_1D
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 263210
        num_examples: 30
    download_size: 132084
    dataset_size: 263210
  - config_name: M_DENSE_1H
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 6307610
        num_examples: 30
    download_size: 2055774
    dataset_size: 6307610
  - config_name: SZ_TAXI_15T
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 5573777
        num_examples: 156
    download_size: 2632475
    dataset_size: 5573777
  - config_name: SZ_TAXI_1H
    features:
      - name: target
        sequence: float32
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
    splits:
      - name: train
        num_bytes: 1395473
        num_examples: 156
    download_size: 728438
    dataset_size: 1395473
configs:
  - config_name: ETT_15T
    data_files:
      - split: train
        path: ETT/15T/train-*
  - config_name: ETT_1H
    data_files:
      - split: train
        path: ETT/1H/train-*
  - config_name: ETTh
    data_files:
      - split: train
        path: ETTh/train-*
  - config_name: LOOP_SEATTLE_1D
    data_files:
      - split: train
        path: LOOP_SEATTLE/1D/train-*
  - config_name: LOOP_SEATTLE_1H
    data_files:
      - split: train
        path: LOOP_SEATTLE/1H/train-*
  - config_name: LOOP_SEATTLE_5T
    data_files:
      - split: train
        path: LOOP_SEATTLE/5T/train-*
  - config_name: M_DENSE_1D
    data_files:
      - split: train
        path: M_DENSE/1D/train-*
  - config_name: M_DENSE_1H
    data_files:
      - split: train
        path: M_DENSE/1H/train-*
  - config_name: SZ_TAXI_15T
    data_files:
      - split: train
        path: SZ_TAXI/15T/train-*
  - config_name: SZ_TAXI_1H
    data_files:
      - split: train
        path: SZ_TAXI/1H/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