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fev_datasets

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Time Series Forecasting
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@@ -62,32 +62,6 @@ dataset_info:
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  num_examples: 2
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  download_size: 531145
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  dataset_size: 1254322
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- - config_name: ETTh
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- features:
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- - name: id
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- dtype: string
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- - name: timestamp
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- sequence: timestamp[ns]
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- - name: HUFL
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- sequence: float64
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- - name: HULL
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- sequence: float64
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- - name: MUFL
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- sequence: float64
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- - name: MULL
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- sequence: float64
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- - name: LUFL
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- sequence: float64
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- - name: LULL
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- sequence: float64
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- - name: OT
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- sequence: float64
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- splits:
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- - name: train
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- num_bytes: 2229842
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- num_examples: 2
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- download_size: 569100
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- dataset_size: 2229842
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  - config_name: LOOP_SEATTLE_1D
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  features:
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  - name: target
@@ -4611,10 +4585,6 @@ configs:
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  data_files:
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  - split: train
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  path: ETT/1H/train-*
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- - config_name: ETTh
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- data_files:
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- - split: train
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- path: ETTh/train-*
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  - config_name: LOOP_SEATTLE_1D
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  data_files:
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  - split: train
@@ -5012,10 +4982,10 @@ Datasets can be loaded using the [🤗 `datasets`](https://huggingface.co/docs/d
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  ```python
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  import datasets
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- ds = datasets.load_dataset("autogluon/fev_datasets", "epf_electricity_de", split="train")
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  ds.set_format("numpy") # sequences returned as numpy arrays
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  ```
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- Example entry in the `epf_electricity_de` dataset
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  ```python
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  >>> ds[0]
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  {'id': 'DE',
@@ -5037,27 +5007,103 @@ For more details about the dataset format and usage, check out the [`fev` docume
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  **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.
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- | config | freq | # items | # obs | # dynamic cols | # static cols | source | citation |
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- |:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
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- | `ETTh` | h | 2 | 243880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) |
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- | `ETTm` | 15min | 2 | 975520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) |
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- | `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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- | `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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- | `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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- | `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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- | `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[2]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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- | `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) |
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- | `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) |
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- | `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) |
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- | `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
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- | `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[5]](https://doi.org/10.48550/arXiv.2307.07191) |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Publications using these datasets
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  num_examples: 2
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  download_size: 531145
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  dataset_size: 1254322
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - config_name: LOOP_SEATTLE_1D
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  features:
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  - name: target
 
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  data_files:
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  - split: train
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  path: ETT/1H/train-*
 
 
 
 
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  - config_name: LOOP_SEATTLE_1D
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  data_files:
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  - split: train
 
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  ```python
4983
  import datasets
4984
 
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+ ds = datasets.load_dataset("autogluon/fev_datasets", "epf_de", split="train")
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  ds.set_format("numpy") # sequences returned as numpy arrays
4987
  ```
4988
+ Example entry in the `epf_de` dataset
4989
  ```python
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  >>> ds[0]
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  {'id': 'DE',
 
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  **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.
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+ | config | freq | # items | median length | # obs | # dynamic cols | # static cols | source | citation |
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+ |:---------------------------|:-------|:----------|:----------------|:------------|-----------------:|----------------:|:---------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|
5012
+ | `ETT_15T` | 15min | 2 | 69,680 | 975,520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) |
5013
+ | `ETT_1H` | h | 2 | 17,420 | 243,880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) |
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+ | `LOOP_SEATTLE_1D` | D | 323 | 365 | 117,895 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `LOOP_SEATTLE_1H` | h | 323 | 8,760 | 2,829,480 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `LOOP_SEATTLE_5T` | 5min | 323 | 105,120 | 33,953,760 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
5017
+ | `M_DENSE_1D` | D | 30 | 730 | 21,900 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `M_DENSE_1H` | h | 30 | 17,520 | 525,600 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `SZ_TAXI_15T` | 15min | 156 | 2,976 | 464,256 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `SZ_TAXI_1H` | h | 156 | 744 | 116,064 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) |
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+ | `beijing_air_quality` | h | 12 | 35,064 | 4,628,448 | 11 | 0 | https://huggingface.co/datasets/Salesforce/lotsa_data | [[3]](https://arxiv.org/abs/2402.02592) |
5022
+ | `bizitobs_l2c` | h | 1 | 2,664 | 18,648 | 7 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
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+ | `boomlet_1062` | 5min | 1 | 16,384 | 344,064 | 21 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
5024
+ | `boomlet_1209` | 5min | 1 | 16,384 | 868,352 | 53 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
5025
+ | `boomlet_1225` | min | 1 | 16,384 | 802,816 | 49 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1230` | 5min | 1 | 16,384 | 376,832 | 23 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
5027
+ | `boomlet_1282` | min | 1 | 16,384 | 573,440 | 35 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1487` | 5min | 1 | 16,384 | 884,736 | 54 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1631` | 30min | 1 | 10,463 | 418,520 | 40 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1676` | 30min | 1 | 10,463 | 1,046,300 | 100 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1855` | h | 1 | 5,231 | 272,012 | 52 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_1975` | h | 1 | 5,231 | 392,325 | 75 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_2187` | h | 1 | 5,231 | 523,100 | 100 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_285` | min | 1 | 16,384 | 1,228,800 | 75 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_619` | min | 1 | 16,384 | 851,968 | 52 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_772` | min | 1 | 16,384 | 1,097,728 | 67 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
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+ | `boomlet_963` | min | 1 | 16,384 | 458,752 | 28 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) |
5038
+ | `cdc_fluview_ilinet` | W-SUN | 75 | 680 | 319,515 | 5 | 0 | https://huggingface.co/datasets/Salesforce/lotsa_data | [[3]](https://arxiv.org/abs/2402.02592) |
5039
+ | `ecdc_ili` | W-SUN | 25 | 201 | 4,797 | 1 | 0 | https://github.com/EU-ECDC/Respiratory_viruses_weekly_data/blob/main/data/snapshots/2025-08-08_ILIARIRates.csv | |
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+ | `epf_be` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[6]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `epf_de` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[6]](https://doi.org/10.1016/j.apenergy.2021.116983) |
5042
+ | `epf_fr` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[6]](https://doi.org/10.1016/j.apenergy.2021.116983) |
5043
+ | `epf_np` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[6]](https://doi.org/10.1016/j.apenergy.2021.116983) |
5044
+ | `epf_pjm` | h | 1 | 52,416 | 157,248 | 3 | 0 | https://zenodo.org/records/4624805 | [[6]](https://doi.org/10.1016/j.apenergy.2021.116983) |
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+ | `ercot_1D` | D | 8 | 6,452 | 51,616 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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+ | `ercot_1H` | h | 8 | 154,872 | 1,238,976 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
5047
+ | `ercot_1M` | ME | 8 | 211 | 1,688 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
5048
+ | `ercot_1W` | W-SUN | 8 | 921 | 7,368 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | |
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+ | `favorita_stores_1D` | D | 1,579 | 1,688 | 10,661,408 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5050
+ | `favorita_stores_1M` | ME | 1,579 | 54 | 255,798 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5051
+ | `favorita_stores_1W` | W-SUN | 1,579 | 240 | 1,136,880 | 3 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5052
+ | `favorita_transactions_1D` | D | 51 | 1,688 | 258,264 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5053
+ | `favorita_transactions_1M` | ME | 51 | 54 | 5,508 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5054
+ | `favorita_transactions_1W` | W-SUN | 51 | 240 | 24,480 | 2 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[7]](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview/citation) |
5055
+ | `fred_md_2025` | MS | 1 | 798 | 100,548 | 126 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[8]](https://doi.org/10.20955/wp.2015.012) |
5056
+ | `fred_qd_2025` | QS-DEC | 1 | 266 | 65,170 | 245 | 0 | https://www.stlouisfed.org/research/economists/mccracken/fred-databases | [[9]](https://doi.org/10.20955/wp.2020.005) |
5057
+ | `gvar` | QS-OCT | 33 | 178 | 52,866 | 9 | 0 | https://data.mendeley.com/datasets/kfp5fhgkvf/1 | [[10]](https://doi.org/10.17863/CAM.104755) |
5058
+ | `hermes` | W-MON | 10,000 | 261 | 5,220,000 | 2 | 2 | https://github.com/etidav/HERMES | [[11]](https://arxiv.org/abs/2202.03224) |
5059
+ | `hierarchical_sales_1D` | D | 118 | 1,825 | 215,350 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5060
+ | `hierarchical_sales_1W` | W-WED | 118 | 260 | 30,680 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5061
+ | `hierarchical_tourism` | QE-DEC | 89 | 36 | 3,204 | 1 | 0 | https://robjhyndman.com/publications/hierarchical-tourism/ | [[12]](https://doi.org/10.1016/j.ijforecast.2008.07.004) |
5062
+ | `hospital_admissions_1D` | D | 8 | 1,731 | 13,846 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
5063
+ | `hospital_admissions_1W` | W-SUN | 8 | 246 | 1,968 | 1 | 0 | https://www.kaggle.com/datasets/datasetengineer/riyadh-hospital-admissions-dataset-20202024 | [[13]](https://doi.org/10.34740/kaggle/dsv/9992619) |
5064
+ | `hospital` | ME | 767 | 84 | 64,428 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5065
+ | `jena_weather_10T` | 10min | 1 | 52,704 | 1,106,784 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5066
+ | `jena_weather_1D` | D | 1 | 366 | 7,686 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5067
+ | `jena_weather_1H` | h | 1 | 8,784 | 184,464 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5068
+ | `kdd_cup_2018_1D` | D | 270 | 455 | 122,791 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5069
+ | `kdd_cup_2018_1H` | h | 270 | 10,898 | 2,942,364 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5070
+ | `kdd_cup_2022_10T` | 10min | 134 | 35,279 | 47,273,860 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
5071
+ | `kdd_cup_2022_1D` | D | 134 | 243 | 325,620 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
5072
+ | `kdd_cup_2022_30T` | 30min | 134 | 11,758 | 15,755,720 | 10 | 0 | https://aistudio.baidu.com/competition/detail/152/0/task-definition | [[14]](https://arxiv.org/abs/2208.04360) |
5073
+ | `m5_1D` | D | 30,490 | 1,810 | 428,849,460 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
5074
+ | `m5_1M` | ME | 30,490 | 58 | 13,805,685 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
5075
+ | `m5_1W` | W-SUN | 30,490 | 257 | 60,857,703 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013) |
5076
+ | `proenfo_bull` | h | 41 | 17,544 | 2,877,216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5077
+ | `proenfo_cockatoo` | h | 1 | 17,544 | 105,264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5078
+ | `proenfo_gfc12` | h | 11 | 39,414 | 867,108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5079
+ | `proenfo_gfc14` | h | 1 | 17,520 | 35,040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5080
+ | `proenfo_gfc17` | h | 8 | 17,544 | 280,704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5081
+ | `proenfo_hog` | h | 24 | 17,544 | 2,526,336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5082
+ | `proenfo_pdb` | h | 1 | 17,520 | 35,040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[16]](https://doi.org/10.48550/arXiv.2307.07191) |
5083
+ | `redset_15T` | 15min | 126 | 8,640 | 1,052,371 | 1 | 1 | https://github.com/amazon-science/redset/ | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
5084
+ | `redset_1H` | h | 138 | 2,160 | 283,070 | 1 | 1 | https://github.com/amazon-science/redset/ | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
5085
+ | `redset_5T` | 5min | 118 | 25,920 | 2,960,408 | 1 | 1 | https://github.com/amazon-science/redset/ | [[17]](https://www.amazon.science/publications/why-tpc-is-not-enough-an-analysis-of-the-amazon-redshift-fleet) |
5086
+ | `restaurant` | D | 817 | 296 | 294,568 | 1 | 4 | https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting | [[18]](www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/overview/citation) |
5087
+ | `rossmann_1D` | D | 1,115 | 942 | 7,352,310 | 7 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[19]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
5088
+ | `rossmann_1W` | W-SUN | 1,115 | 133 | 889,770 | 6 | 10 | https://www.kaggle.com/competitions/rossmann-store-sales | [[19]](www.kaggle.com/competitions/rossmann-store-sales/overview/citation) |
5089
+ | `solar_10T` | 10min | 137 | 52,560 | 7,200,720 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5090
+ | `solar_1D` | D | 137 | 365 | 50,005 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5091
+ | `solar_1H` | h | 137 | 8,760 | 1,200,120 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5092
+ | `solar_1W` | W-FRI | 137 | 52 | 7,124 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) |
5093
+ | `solar_with_weather_15T` | 15min | 1 | 198,600 | 1,986,000 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
5094
+ | `solar_with_weather_1H` | h | 1 | 49,648 | 496,480 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | |
5095
+ | `uci_air_quality` | h | 1 | 9,357 | 121,641 | 13 | 0 | https://archive.ics.uci.edu/dataset/360/air+quality | [[20]](https://doi.org/10.24432/C59K5F) |
5096
+ | `uk_covid_nation_1D` | D | 4 | 729 | 41,216 | 14 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | |
5097
+ | `uk_covid_nation_1W` | W-SUN | 4 | 105 | 5,936 | 14 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | |
5098
+ | `uk_covid_utla_1D` | D | 214 | 721 | 308,786 | 2 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | |
5099
+ | `uk_covid_utla_1W` | W-SUN | 214 | 104 | 44,448 | 2 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | |
5100
+ | `us_consumption_1M` | MS | 31 | 792 | 24,552 | 1 | 0 | https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying | [[21]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
5101
+ | `us_consumption_1Q` | QE-DEC | 31 | 262 | 8,122 | 1 | 0 | https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying | [[21]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
5102
+ | `us_consumption_1Y` | YE-DEC | 31 | 64 | 1,984 | 1 | 0 | https://apps.bea.gov/iTable/?reqid=19&step=3&isuri=1&nipa_table_list=2017&categories=underlying | [[21]](https://doi.org/10.1016/j.ijforecast.2016.04.005) |
5103
+ | `walmart` | W-FRI | 2,936 | 143 | 4,609,143 | 11 | 4 | https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting | [[22]](www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/overview/citation) |
5104
+ | `world_co2_emissions` | YE-DEC | 191 | 60 | 11,460 | 1 | 0 | https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country | |
5105
+ | `world_life_expectancy` | YE-DEC | 237 | 74 | 17,538 | 1 | 0 | https://www.kaggle.com/datasets/nafayunnoor/global-life-expectancy-data-1950-2023 | [[23]](https://ourworldindata.org/life-expectancy#article-citation) |
5106
+ | `world_tourism` | YE-DEC | 178 | 21 | 3,738 | 1 | 0 | https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact | |
5107
 
5108
  ## Publications using these datasets
5109