Datasets:
File size: 22,555 Bytes
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---
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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- config_name: beijing_air_quality
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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
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- split: train
path: LOOP_SEATTLE/1H/train-*
- config_name: LOOP_SEATTLE_5T
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- split: train
path: LOOP_SEATTLE/5T/train-*
- config_name: M_DENSE_1D
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- split: train
path: M_DENSE/1D/train-*
- config_name: M_DENSE_1H
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- split: train
path: M_DENSE/1H/train-*
- config_name: SZ_TAXI_15T
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- 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`](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)
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