README.md

---
annotations_creators:
- no-annotation
license: other
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
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    sequence: float32
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    sequence: float32
  - name: S&P 500
    sequence: float32
  - name: S&P div yield
    sequence: float32
  - name: S&P PE ratio
    sequence: float32
  splits:
  - name: train
    num_bytes: 263811
    num_examples: 1
  download_size: 446720
  dataset_size: 263811
- config_name: gvar
  features:
  - name: id
    dtype: string
  - name: timestamp
    sequence: timestamp[ms]
  - name: y
    sequence: float32
  - name: Dp
    sequence: float32
  - name: eq
    sequence: float32
  - name: ep
    sequence: float32
  - name: r
    sequence: float32
  - name: lr
    sequence: float32
  - name: poil
    sequence: float32
  - name: pmat
    sequence: float32
  - name: pmetal
    sequence: float32
  splits:
  - name: train
    num_bytes: 260043
    num_examples: 33
  download_size: 178418
  dataset_size: 260043
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-*
- config_name: boomlet_1282
  data_files:
  - split: train
    path: boomlet/1282/train-*
- config_name: boomlet_1487
  data_files:
  - split: train
    path: boomlet/1487/train-*
- config_name: boomlet_1631
  data_files:
  - split: train
    path: boomlet/1631/train-*
- config_name: boomlet_1676
  data_files:
  - split: train
    path: boomlet/1676/train-*
- config_name: boomlet_1855
  data_files:
  - split: train
    path: boomlet/1855/train-*
- config_name: boomlet_1975
  data_files:
  - split: train
    path: boomlet/1975/train-*
- config_name: boomlet_2187
  data_files:
  - split: train
    path: boomlet/2187/train-*
- config_name: boomlet_285
  data_files:
  - split: train
    path: boomlet/285/train-*
- config_name: boomlet_619
  data_files:
  - split: train
    path: boomlet/619/train-*
- config_name: boomlet_772
  data_files:
  - split: train
    path: boomlet/772/train-*
- config_name: boomlet_963
  data_files:
  - split: train
    path: boomlet/963/train-*
- config_name: cdc_fluview_ilinet
  data_files:
  - split: train
    path: cdc_fluview_ilinet/train-*
- config_name: ecdc_ili
  data_files:
  - split: train
    path: ecdc_ili/train-*
- config_name: epf_be
  data_files:
  - split: train
    path: epf_be/train-*
- config_name: epf_de
  data_files:
  - split: train
    path: epf_de/train-*
- config_name: epf_fr
  data_files:
  - split: train
    path: epf_fr/train-*
- config_name: epf_np
  data_files:
  - split: train
    path: epf_np/train-*
- config_name: epf_pjm
  data_files:
  - split: train
    path: epf_pjm/train-*
- config_name: ercot_1D
  data_files:
  - split: train
    path: ercot/1D/train-*
- config_name: ercot_1H
  data_files:
  - split: train
    path: ercot/1H/train-*
- config_name: ercot_1M
  data_files:
  - split: train
    path: ercot/1M/train-*
- config_name: ercot_1W
  data_files:
  - split: train
    path: ercot/1W/train-*
- config_name: favorita_stores_1D
  data_files:
  - split: train
    path: favorita_stores/1D/train-*
- config_name: favorita_stores_1M
  data_files:
  - split: train
    path: favorita_stores/1M/train-*
- config_name: favorita_stores_1W
  data_files:
  - split: train
    path: favorita_stores/1W/train-*
- config_name: favorita_transactions_1D
  data_files:
  - split: train
    path: favorita_transactions/1D/train-*
- config_name: favorita_transactions_1M
  data_files:
  - split: train
    path: favorita_transactions/1M/train-*
- config_name: favorita_transactions_1W
  data_files:
  - split: train
    path: favorita_transactions/1W/train-*
- config_name: fred_md_2025
  data_files:
  - split: train
    path: fred_md_2025/train-*
- config_name: fred_qd_2025
  data_files:
  - split: train
    path: fred_qd_2025/train-*
- config_name: gvar
  data_files:
  - split: train
    path: gvar/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`](https://github.com/autogluon/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`](https://huggingface.co/docs/datasets/en/index) library.

```python
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
```python
>>> 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](https://github.com/autogluon/fev?tab=readme-ov-file#tutorials).

## 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]](https://arxiv.org/abs/2012.07436)                                                          |
| `ETTm`                  | 15min  |         2 |    975520 |                7 |               0 | https://github.com/zhouhaoyi/ETDataset                                  | [[1]](https://arxiv.org/abs/2012.07436)                                                          |
| `epf_electricity_be`    | h      |         1 |    157248 |                3 |               0 | https://zenodo.org/records/4624805                                      | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983)                                            |
| `epf_electricity_de`    | h      |         1 |    157248 |                3 |               0 | https://zenodo.org/records/4624805                                      | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983)                                            |
| `epf_electricity_fr`    | h      |         1 |    157248 |                3 |               0 | https://zenodo.org/records/4624805                                      | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983)                                            |
| `epf_electricity_np`    | h      |         1 |    157248 |                3 |               0 | https://zenodo.org/records/4624805                                      | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983)                                            |
| `epf_electricity_pjm`   | h      |         1 |    157248 |                3 |               0 | https://zenodo.org/records/4624805                                      | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983)                                            |
| `favorita_store_sales`  | D      |      1782 |  12032064 |                4 |               6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[3]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
| `favorita_transactions` | D      |        54 |    273456 |                3 |               5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[3]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
| `m5_with_covariates`    | D      |     30490 | 428849460 |                9 |               5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy             | [[4]](https://doi.org/10.1016/j.ijforecast.2021.07.007)                                          |
| `proenfo_bull`          | h      |        41 |   2877216 |                4 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_cockatoo`      | h      |         1 |    105264 |                6 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_covid19`       | h      |         1 |    223384 |                7 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_gfc12_load`    | h      |        11 |    867108 |                2 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_gfc14_load`    | h      |         1 |     35040 |                2 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_gfc17_load`    | h      |         8 |    280704 |                2 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_hog`           | h      |        24 |   2526336 |                6 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_pdb`           | h      |         1 |     35040 |                2 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |
| `proenfo_spain`         | h      |         1 |    736344 |               21 |               0 | https://github.com/Leo-VK/EnFoAV                                        | [[5]](https://doi.org/10.48550/arXiv.2307.07191)                                                 |

## Publications using these datasets

- ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107)