--- 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 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-* --- ## 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)