--- 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 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-* --- ## 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)