Datasets:
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README.md
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@@ -593,25 +593,25 @@ For more details about the dataset format and usage, check out the [`fev` docume
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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]](
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597 |
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| `ETTm` | 15min | 2 | 975520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](
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598 |
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| `epf_electricity_be` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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| `epf_electricity_de` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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600 |
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| `epf_electricity_fr` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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601 |
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| `epf_electricity_np` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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602 |
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| `epf_electricity_pjm` | h | 1 | 157248 | 3 | 0 | https://zenodo.org/records/4624805 | [[
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603 |
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| `favorita_store_sales` | D | 1782 | 12032064 | 4 | 6 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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| `favorita_transactions` | D | 54 | 273456 | 3 | 5 | https://www.kaggle.com/competitions/store-sales-time-series-forecasting | [[
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605 |
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| `m5_with_covariates` | D | 30490 | 428849460 | 9 | 5 | https://www.kaggle.com/competitions/m5-forecasting-accuracy | [[
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606 |
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| `proenfo_bull` | h | 41 | 2877216 | 4 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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607 |
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| `proenfo_cockatoo` | h | 1 | 105264 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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608 |
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| `proenfo_covid19` | h | 1 | 223384 | 7 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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609 |
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| `proenfo_gfc12_load` | h | 11 | 867108 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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610 |
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| `proenfo_gfc14_load` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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611 |
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| `proenfo_gfc17_load` | h | 8 | 280704 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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612 |
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| `proenfo_hog` | h | 24 | 2526336 | 6 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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613 |
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| `proenfo_pdb` | h | 1 | 35040 | 2 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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| `proenfo_spain` | h | 1 | 736344 | 21 | 0 | https://github.com/Leo-VK/EnFoAV | [[
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## Publications using these datasets
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|
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| config | freq | # items | # obs | # dynamic cols | # static cols | source | citation |
|
595 |
|:------------------------|:-------|----------:|----------:|-----------------:|----------------:|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
|
596 |
+
| `ETTh` | h | 2 | 243880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) |
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597 |
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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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598 |
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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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599 |
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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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603 |
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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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604 |
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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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605 |
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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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606 |
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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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607 |
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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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608 |
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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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609 |
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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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610 |
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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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611 |
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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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612 |
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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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617 |
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