Datasets:

autogluon
/

fev_datasets

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
  - config_name: beijing_air_quality
    features:
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
      - name: target_7
        sequence: float32
      - name: target_8
        sequence: float32
      - name: target_9
        sequence: float32
      - name: target_10
        sequence: float32
    splits:
      - name: train
        num_bytes: 21880997
        num_examples: 12
    download_size: 6008430
    dataset_size: 21880997
  - config_name: bizitobs_l2c
    features:
      - name: id
        dtype: string
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
    splits:
      - name: train
        num_bytes: 95943
        num_examples: 1
    download_size: 60364
    dataset_size: 95943
  - config_name: boomlet_1062
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: Application Usage
        dtype: float32
      - name: Infrastructure
        dtype: float32
      - name: Database
        dtype: float32
      - name: Networking
        dtype: float32
      - name: Security
        dtype: float32
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
      - name: target_7
        sequence: float32
      - name: target_8
        sequence: float32
      - name: target_9
        sequence: float32
      - name: target_10
        sequence: float32
      - name: target_11
        sequence: float32
      - name: target_12
        sequence: float32
      - name: target_13
        sequence: float32
      - name: target_14
        sequence: float32
      - name: target_15
        sequence: float32
      - name: target_16
        sequence: float32
      - name: target_17
        sequence: float32
      - name: target_18
        sequence: float32
      - name: target_19
        sequence: float32
      - name: target_20
        sequence: float32
    splits:
      - name: train
        num_bytes: 1507449
        num_examples: 1
    download_size: 591056
    dataset_size: 1507449
  - config_name: boomlet_1209
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: Application Usage
        dtype: float32
      - name: Infrastructure
        dtype: float32
      - name: Database
        dtype: float32
      - name: Networking
        dtype: float32
      - name: Security
        dtype: float32
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
      - name: target_7
        sequence: float32
      - name: target_8
        sequence: float32
      - name: target_9
        sequence: float32
      - name: target_10
        sequence: float32
      - name: target_11
        sequence: float32
      - name: target_12
        sequence: float32
      - name: target_13
        sequence: float32
      - name: target_14
        sequence: float32
      - name: target_15
        sequence: float32
      - name: target_16
        sequence: float32
      - name: target_17
        sequence: float32
      - name: target_18
        sequence: float32
      - name: target_19
        sequence: float32
      - name: target_20
        sequence: float32
      - name: target_21
        sequence: float32
      - name: target_22
        sequence: float32
      - name: target_23
        sequence: float32
      - name: target_24
        sequence: float32
      - name: target_25
        sequence: float32
      - name: target_26
        sequence: float32
      - name: target_27
        sequence: float32
      - name: target_28
        sequence: float32
      - name: target_29
        sequence: float32
      - name: target_30
        sequence: float32
      - name: target_31
        sequence: float32
      - name: target_32
        sequence: float32
      - name: target_33
        sequence: float32
      - name: target_34
        sequence: float32
      - name: target_35
        sequence: float32
      - name: target_36
        sequence: float32
      - name: target_37
        sequence: float32
      - name: target_38
        sequence: float32
      - name: target_39
        sequence: float32
      - name: target_40
        sequence: float32
      - name: target_41
        sequence: float32
      - name: target_42
        sequence: float32
      - name: target_43
        sequence: float32
      - name: target_44
        sequence: float32
      - name: target_45
        sequence: float32
      - name: target_46
        sequence: float32
      - name: target_47
        sequence: float32
      - name: target_48
        sequence: float32
      - name: target_49
        sequence: float32
      - name: target_50
        sequence: float32
      - name: target_51
        sequence: float32
      - name: target_52
        sequence: float32
    splits:
      - name: train
        num_bytes: 3604729
        num_examples: 1
    download_size: 736133
    dataset_size: 3604729
  - config_name: boomlet_1225
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: Application Usage
        dtype: float32
      - name: Infrastructure
        dtype: float32
      - name: Database
        dtype: float32
      - name: Networking
        dtype: float32
      - name: Security
        dtype: float32
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
      - name: target_7
        sequence: float32
      - name: target_8
        sequence: float32
      - name: target_9
        sequence: float32
      - name: target_10
        sequence: float32
      - name: target_11
        sequence: float32
      - name: target_12
        sequence: float32
      - name: target_13
        sequence: float32
      - name: target_14
        sequence: float32
      - name: target_15
        sequence: float32
      - name: target_16
        sequence: float32
      - name: target_17
        sequence: float32
      - name: target_18
        sequence: float32
      - name: target_19
        sequence: float32
      - name: target_20
        sequence: float32
      - name: target_21
        sequence: float32
      - name: target_22
        sequence: float32
      - name: target_23
        sequence: float32
      - name: target_24
        sequence: float32
      - name: target_25
        sequence: float32
      - name: target_26
        sequence: float32
      - name: target_27
        sequence: float32
      - name: target_28
        sequence: float32
      - name: target_29
        sequence: float32
      - name: target_30
        sequence: float32
      - name: target_31
        sequence: float32
      - name: target_32
        sequence: float32
      - name: target_33
        sequence: float32
      - name: target_34
        sequence: float32
      - name: target_35
        sequence: float32
      - name: target_36
        sequence: float32
      - name: target_37
        sequence: float32
      - name: target_38
        sequence: float32
      - name: target_39
        sequence: float32
      - name: target_40
        sequence: float32
      - name: target_41
        sequence: float32
      - name: target_42
        sequence: float32
      - name: target_43
        sequence: float32
      - name: target_44
        sequence: float32
      - name: target_45
        sequence: float32
      - name: target_46
        sequence: float32
      - name: target_47
        sequence: float32
      - name: target_48
        sequence: float32
    splits:
      - name: train
        num_bytes: 3342569
        num_examples: 1
    download_size: 4775569
    dataset_size: 3342569
  - config_name: boomlet_1230
    features:
      - name: id
        dtype: string
      - name: type
        dtype: string
      - name: Application Usage
        dtype: float32
      - name: Infrastructure
        dtype: float32
      - name: Database
        dtype: float32
      - name: Networking
        dtype: float32
      - name: Security
        dtype: float32
      - name: timestamp
        sequence: timestamp[ms]
      - name: target_0
        sequence: float32
      - name: target_1
        sequence: float32
      - name: target_2
        sequence: float32
      - name: target_3
        sequence: float32
      - name: target_4
        sequence: float32
      - name: target_5
        sequence: float32
      - name: target_6
        sequence: float32
      - name: target_7
        sequence: float32
      - name: target_8
        sequence: float32
      - name: target_9
        sequence: float32
      - name: target_10
        sequence: float32
      - name: target_11
        sequence: float32
      - name: target_12
        sequence: float32
      - name: target_13
        sequence: float32
      - name: target_14
        sequence: float32
      - name: target_15
        sequence: float32
      - name: target_16
        sequence: float32
      - name: target_17
        sequence: float32
      - name: target_18
        sequence: float32
      - name: target_19
        sequence: float32
      - name: target_20
        sequence: float32
      - name: target_21
        sequence: float32
      - name: target_22
        sequence: float32
    splits:
      - name: train
        num_bytes: 1638529
        num_examples: 1
    download_size: 2311388
    dataset_size: 1638529
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-*
  - config_name: beijing_air_quality
    data_files:
      - split: train
        path: beijing_air_quality/train-*
  - config_name: bizitobs_l2c
    data_files:
      - split: train
        path: bizitobs_l2c/train-*
  - config_name: boomlet_1062
    data_files:
      - split: train
        path: boomlet/1062/train-*
  - config_name: boomlet_1209
    data_files:
      - split: train
        path: boomlet/1209/train-*
  - config_name: boomlet_1225
    data_files:
      - split: train
        path: boomlet/1225/train-*
  - config_name: boomlet_1230
    data_files:
      - split: train
        path: boomlet/1230/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

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