Datasets:
File size: 18,215 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
dataset_info:
- config_name: epf_electricity_be
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[us]
- name: target
sequence: float64
- name: Generation forecast
sequence: float64
- name: System load forecast
sequence: float64
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- config_name: epf_electricity_de
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[us]
- name: target
sequence: float64
- name: Ampirion Load Forecast
sequence: float64
- name: PV+Wind Forecast
sequence: float64
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- config_name: epf_electricity_fr
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[us]
- name: target
sequence: float64
- name: Generation forecast
sequence: float64
- name: System load forecast
sequence: float64
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- config_name: epf_electricity_np
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[us]
- name: target
sequence: float64
- name: Grid load forecast
sequence: float64
- name: Wind power forecast
sequence: float64
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- config_name: epf_electricity_pjm
features:
- name: id
dtype: string
- name: timestamp
sequence: timestamp[us]
- name: target
sequence: float64
- name: System load forecast
sequence: float64
- name: Zonal COMED load foecast
sequence: float64
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- config_name: favorita_store_sales
features:
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dtype: string
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sequence: timestamp[us]
- name: sales
sequence: float64
- name: onpromotion
sequence: int64
- name: oil_price
sequence: float64
- name: holiday
sequence: string
- name: store_nbr
dtype: int64
- name: family
dtype: string
- name: city
dtype: string
- name: state
dtype: string
- name: type
dtype: string
- name: cluster
dtype: int64
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- config_name: favorita_transactions
features:
- name: id
dtype: int64
- name: timestamp
sequence: timestamp[us]
- name: transactions
sequence: int64
- name: oil_price
sequence: float64
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sequence: string
- name: store_nbr
dtype: int64
- name: city
dtype: string
- name: state
dtype: string
- name: type
dtype: string
- name: cluster
dtype: int64
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- config_name: m5_with_covariates
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- name: target
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- name: snap_CA
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- name: snap_TX
sequence: int64
- name: snap_WI
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- name: sell_price
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- name: event_Cultural
sequence: int64
- name: event_National
sequence: int64
- name: event_Religious
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- name: event_Sporting
sequence: int64
- name: item_id
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- name: dept_id
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- name: cat_id
dtype: string
- name: store_id
dtype: string
- name: state_id
dtype: string
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- name: airtemperature
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- name: sealvlpressure
sequence: float64
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- name: winddirection
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- name: windspeed
sequence: float64
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- name: pressure_kpa
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- name: humidity_perc
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- name: airtemperature
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- name: wind_direction_deg
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- name: wind_speed_kmh
sequence: float64
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- config_name: proenfo_gfc12_load
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- name: target
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- name: airtemperature
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- name: airtemperature
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- name: airtemperature
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- name: airtemperature
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- name: winddirection
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- name: windspeed
sequence: float64
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- config_name: proenfo_pdb
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- name: airtemperature
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- config_name: proenfo_spain
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- name: target
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- name: generation_biomass
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- name: generation_fossil_brown_coal_lignite
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- name: generation_fossil_coal_derived_gas
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- name: generation_fossil_gas
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- name: generation_fossil_hard_coal
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- name: generation_fossil_oil
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- name: generation_fossil_oil_shale
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- name: generation_fossil_peat
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- name: generation_geothermal
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- name: generation_hydro_pumped_storage_consumption
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- name: generation_hydro_run_of_river_and_poundage
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- name: generation_hydro_water_reservoir
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- name: generation_marine
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- name: generation_nuclear
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- name: generation_other
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- name: generation_other_renewable
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- name: generation_solar
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- name: generation_waste
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- name: generation_wind_offshore
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- name: generation_wind_onshore
sequence: float64
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configs:
- config_name: epf_electricity_be
data_files:
- split: train
path: epf/electricity_be/train-*
- config_name: epf_electricity_de
data_files:
- split: train
path: epf/electricity_de/train-*
- config_name: epf_electricity_fr
data_files:
- split: train
path: epf/electricity_fr/train-*
- config_name: epf_electricity_np
data_files:
- split: train
path: epf/electricity_np/train-*
- config_name: epf_electricity_pjm
data_files:
- split: train
path: epf/electricity_pjm/train-*
- config_name: favorita_store_sales
data_files:
- split: train
path: favorita/store_sales/train-*
- config_name: favorita_transactions
data_files:
- split: train
path: favorita/transactions/train-*
- config_name: m5_with_covariates
data_files:
- split: train
path: m5_with_covariates/train-*
- config_name: proenfo_bull
data_files:
- split: train
path: proenfo/bull/train-*
- config_name: proenfo_cockatoo
data_files:
- split: train
path: proenfo/cockatoo/train-*
- config_name: proenfo_covid19
data_files:
- split: train
path: proenfo/covid19/train-*
- config_name: proenfo_gfc12_load
data_files:
- split: train
path: proenfo/gfc12_load/train-*
- config_name: proenfo_gfc14_load
data_files:
- split: train
path: proenfo/gfc14_load/train-*
- config_name: proenfo_gfc17_load
data_files:
- split: train
path: proenfo/gfc17_load/train-*
- config_name: proenfo_hog
data_files:
- split: train
path: proenfo/hog/train-*
- config_name: proenfo_pdb
data_files:
- split: train
path: proenfo/pdb/train-*
- config_name: proenfo_spain
data_files:
- split: train
path: proenfo/spain/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.
| config | freq | # items | # obs | # dynamic cols | # static cols | source | citation |
|:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
| `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
| `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
| `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](https://doi.org/10.1016/j.apenergy.2021.116983) |
| `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[1]](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 | [[2]](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 | [[2]](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 | [[3]](https://doi.org/10.1016/j.ijforecast.2021.07.007) |
| `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](https://doi.org/10.48550/arXiv.2307.07191) |
| `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[4]](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) |