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--- |
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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-* |
|
|
- config_name: boomlet_1225 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1225/train-* |
|
|
- config_name: boomlet_1230 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1230/train-* |
|
|
- config_name: boomlet_1282 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1282/train-* |
|
|
- config_name: boomlet_1487 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1487/train-* |
|
|
- config_name: boomlet_1631 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1631/train-* |
|
|
- config_name: boomlet_1676 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1676/train-* |
|
|
- config_name: boomlet_1855 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1855/train-* |
|
|
- config_name: boomlet_1975 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/1975/train-* |
|
|
- config_name: boomlet_2187 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/2187/train-* |
|
|
- config_name: boomlet_285 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/285/train-* |
|
|
- config_name: boomlet_619 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/619/train-* |
|
|
- config_name: boomlet_772 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/772/train-* |
|
|
- config_name: boomlet_963 |
|
|
data_files: |
|
|
- split: train |
|
|
path: boomlet/963/train-* |
|
|
- config_name: cdc_fluview_ilinet |
|
|
data_files: |
|
|
- split: train |
|
|
path: cdc_fluview_ilinet/train-* |
|
|
- config_name: ecdc_ili |
|
|
data_files: |
|
|
- split: train |
|
|
path: ecdc_ili/train-* |
|
|
- config_name: epf_be |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf_be/train-* |
|
|
- config_name: epf_de |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf_de/train-* |
|
|
- config_name: epf_fr |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf_fr/train-* |
|
|
- config_name: epf_np |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf_np/train-* |
|
|
- config_name: epf_pjm |
|
|
data_files: |
|
|
- split: train |
|
|
path: epf_pjm/train-* |
|
|
- config_name: ercot_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: ercot/1D/train-* |
|
|
- config_name: ercot_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: ercot/1H/train-* |
|
|
- config_name: ercot_1M |
|
|
data_files: |
|
|
- split: train |
|
|
path: ercot/1M/train-* |
|
|
- config_name: ercot_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: ercot/1W/train-* |
|
|
- config_name: favorita_stores_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_stores/1D/train-* |
|
|
- config_name: favorita_stores_1M |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_stores/1M/train-* |
|
|
- config_name: favorita_stores_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_stores/1W/train-* |
|
|
- config_name: favorita_transactions_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_transactions/1D/train-* |
|
|
- config_name: favorita_transactions_1M |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_transactions/1M/train-* |
|
|
- config_name: favorita_transactions_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: favorita_transactions/1W/train-* |
|
|
- config_name: fred_md_2025 |
|
|
data_files: |
|
|
- split: train |
|
|
path: fred_md_2025/train-* |
|
|
- config_name: fred_qd_2025 |
|
|
data_files: |
|
|
- split: train |
|
|
path: fred_qd_2025/train-* |
|
|
- config_name: gvar |
|
|
data_files: |
|
|
- split: train |
|
|
path: gvar/train-* |
|
|
- config_name: hermes |
|
|
data_files: |
|
|
- split: train |
|
|
path: hermes/train-* |
|
|
- config_name: hierarchical_sales_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: hierarchical_sales/1D/train-* |
|
|
- config_name: hierarchical_sales_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: hierarchical_sales/1W/train-* |
|
|
- config_name: hierarchical_tourism |
|
|
data_files: |
|
|
- split: train |
|
|
path: hierarchical_tourism/train-* |
|
|
- config_name: hospital |
|
|
data_files: |
|
|
- split: train |
|
|
path: hospital/train-* |
|
|
- config_name: hospital_admissions_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: hospital_admissions/1D/train-* |
|
|
- config_name: hospital_admissions_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: hospital_admissions/1W/train-* |
|
|
- config_name: jena_weather_10T |
|
|
data_files: |
|
|
- split: train |
|
|
path: jena_weather/10T/train-* |
|
|
- config_name: jena_weather_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: jena_weather/1D/train-* |
|
|
- config_name: jena_weather_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: jena_weather/1H/train-* |
|
|
- config_name: kdd_cup_2018_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: kdd_cup_2018/1D/train-* |
|
|
- config_name: kdd_cup_2018_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: kdd_cup_2018/1H/train-* |
|
|
- config_name: kdd_cup_2022_10T |
|
|
data_files: |
|
|
- split: train |
|
|
path: kdd_cup_2022/10T/train-* |
|
|
- config_name: kdd_cup_2022_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: kdd_cup_2022/1D/train-* |
|
|
- config_name: kdd_cup_2022_30T |
|
|
data_files: |
|
|
- split: train |
|
|
path: kdd_cup_2022/30T/train-* |
|
|
- config_name: m5_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: m5/1D/train-* |
|
|
- config_name: m5_1M |
|
|
data_files: |
|
|
- split: train |
|
|
path: m5/1M/train-* |
|
|
- config_name: m5_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: m5/1W/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_gfc12 |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo_gfc12/train-* |
|
|
- config_name: proenfo_gfc14 |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo_gfc14/train-* |
|
|
- config_name: proenfo_gfc17 |
|
|
data_files: |
|
|
- split: train |
|
|
path: proenfo_gfc17/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: redset_15T |
|
|
data_files: |
|
|
- split: train |
|
|
path: redset/15T/train-* |
|
|
- config_name: redset_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: redset/1H/train-* |
|
|
- config_name: redset_5T |
|
|
data_files: |
|
|
- split: train |
|
|
path: redset/5T/train-* |
|
|
- config_name: restaurant |
|
|
data_files: |
|
|
- split: train |
|
|
path: restaurant/train-* |
|
|
- config_name: rossmann_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: rossmann/1D/train-* |
|
|
- config_name: rossmann_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: rossmann/1W/train-* |
|
|
- config_name: solar_10T |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar/10T/train-* |
|
|
- config_name: solar_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar/1D/train-* |
|
|
- config_name: solar_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar/1H/train-* |
|
|
- config_name: solar_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar/1W/train-* |
|
|
- config_name: solar_with_weather_15T |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar_with_weather/15T/train-* |
|
|
- config_name: solar_with_weather_1H |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar_with_weather/1H/train-* |
|
|
- config_name: uci_air_quality |
|
|
data_files: |
|
|
- split: train |
|
|
path: uci_air_quality/train-* |
|
|
- config_name: uk_covid_nation_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: uk_covid_nation/1D/train-* |
|
|
- config_name: uk_covid_nation_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: uk_covid_nation/1W/train-* |
|
|
- config_name: uk_covid_utla_1D |
|
|
data_files: |
|
|
- split: train |
|
|
path: uk_covid_utla/1D/train-* |
|
|
- config_name: uk_covid_utla_1W |
|
|
data_files: |
|
|
- split: train |
|
|
path: uk_covid_utla/1W/train-* |
|
|
- config_name: us_consumption_1M |
|
|
data_files: |
|
|
- split: train |
|
|
path: us_consumption/1M/train-* |
|
|
- config_name: us_consumption_1Q |
|
|
data_files: |
|
|
- split: train |
|
|
path: us_consumption/1Q/train-* |
|
|
- config_name: us_consumption_1Y |
|
|
data_files: |
|
|
- split: train |
|
|
path: us_consumption/1Y/train-* |
|
|
- config_name: walmart |
|
|
data_files: |
|
|
- split: train |
|
|
path: walmart/train-* |
|
|
- config_name: world_co2_emissions |
|
|
data_files: |
|
|
- split: train |
|
|
path: world_co2_emissions/train-* |
|
|
- config_name: world_life_expectancy |
|
|
data_files: |
|
|
- split: train |
|
|
path: world_life_expectancy/train-* |
|
|
- config_name: world_tourism |
|
|
data_files: |
|
|
- split: train |
|
|
path: world_tourism/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_de", split="train") |
|
|
ds.set_format("numpy") # sequences returned as numpy arrays |
|
|
``` |
|
|
Example entry in the `epf_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 | median length | # obs | # dynamic cols | # static cols | source | citation | |
|
|
|:---------------------------|:-------|----------:|----------------:|------------:|-----------------:|----------------:|:---------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| |
|
|
| `ETT_15T` | 15min | 2 | 69,680 | 975,520 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | |
|
|
| `ETT_1H` | h | 2 | 17,420 | 243,880 | 7 | 0 | https://github.com/zhouhaoyi/ETDataset | [[1]](https://arxiv.org/abs/2012.07436) | |
|
|
| `LOOP_SEATTLE_1D` | D | 323 | 365 | 117,895 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) | |
|
|
| `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) | |
|
|
| `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) | |
|
|
| `M_DENSE_1D` | D | 30 | 730 | 21,900 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) | |
|
|
| `M_DENSE_1H` | h | 30 | 17,520 | 525,600 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) | |
|
|
| `SZ_TAXI_15T` | 15min | 156 | 2,976 | 464,256 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) | |
|
|
| `SZ_TAXI_1H` | h | 156 | 744 | 116,064 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[2]](https://arxiv.org/abs/2304.14343) | |
|
|
| `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) | |
|
|
| `bizitobs_l2c` | h | 1 | 2,664 | 18,648 | 7 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
|
|
| `boomlet_1062` | 5min | 1 | 16,384 | 344,064 | 21 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `boomlet_1209` | 5min | 1 | 16,384 | 868,352 | 53 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `boomlet_1225` | min | 1 | 16,384 | 802,816 | 49 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `boomlet_1230` | 5min | 1 | 16,384 | 376,832 | 23 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `boomlet_1282` | min | 1 | 16,384 | 573,440 | 35 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `boomlet_1487` | 5min | 1 | 16,384 | 884,736 | 54 | 6 | https://huggingface.co/datasets/Datadog/BOOM | [[5]](https://arxiv.org/abs/2505.14766) | |
|
|
| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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 | | |
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| `ercot_1M` | ME | 8 | 211 | 1,688 | 1 | 0 | https://github.com/ourownstory/neuralprophet-data/tree/main/datasets_raw/energy | | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `gvar` | QS-OCT | 33 | 178 | 52,866 | 9 | 0 | https://data.mendeley.com/datasets/kfp5fhgkvf/1 | [[10]](https://doi.org/10.17863/CAM.104755) | |
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| `hermes` | W-MON | 10,000 | 261 | 5,220,000 | 2 | 2 | https://github.com/etidav/HERMES | [[11]](https://arxiv.org/abs/2202.03224) | |
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| `hierarchical_sales_1D` | D | 118 | 1,825 | 215,350 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `hierarchical_sales_1W` | W-WED | 118 | 260 | 30,680 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `hospital` | ME | 767 | 84 | 64,428 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `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) | |
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| `jena_weather_1D` | D | 1 | 366 | 7,686 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `jena_weather_1H` | h | 1 | 8,784 | 184,464 | 21 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `kdd_cup_2018_1D` | D | 270 | 455 | 122,791 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `solar_10T` | 10min | 137 | 52,560 | 7,200,720 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `solar_1D` | D | 137 | 365 | 50,005 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `solar_1H` | h | 137 | 8,760 | 1,200,120 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `solar_1W` | W-FRI | 137 | 52 | 7,124 | 1 | 0 | https://huggingface.co/datasets/Salesforce/GiftEval | [[4]](https://arxiv.org/abs/2410.10393) | |
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| `solar_with_weather_15T` | 15min | 1 | 198,600 | 1,986,000 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | | |
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| `solar_with_weather_1H` | h | 1 | 49,648 | 496,480 | 10 | 0 | https://www.kaggle.com/datasets/samanemami/renewable-energy-and-weather-conditions | | |
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| `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) | |
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| `uk_covid_nation_1D` | D | 4 | 729 | 41,216 | 14 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | | |
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| `uk_covid_nation_1W` | W-SUN | 4 | 105 | 5,936 | 14 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | | |
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| `uk_covid_utla_1D` | D | 214 | 721 | 308,786 | 2 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | | |
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| `uk_covid_utla_1W` | W-SUN | 214 | 104 | 44,448 | 2 | 0 | https://www.kaggle.com/datasets/happyadam73/uk-covid19-dashboard-data-sqlite-compressed | | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `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) | |
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| `world_co2_emissions` | YE-DEC | 191 | 60 | 11,460 | 1 | 0 | https://www.kaggle.com/datasets/ulrikthygepedersen/co2-emissions-by-country | | |
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| `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) | |
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| `world_tourism` | YE-DEC | 178 | 21 | 3,738 | 1 | 0 | https://www.kaggle.com/datasets/bushraqurban/tourism-and-economic-impact | | |
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## Publications using these datasets |
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- ["ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables"](https://arxiv.org/abs/2503.12107) |
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