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--- |
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dataset_info: |
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features: |
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- name: start |
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dtype: timestamp[s] |
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- name: feat_static_cat |
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dtype: uint64 |
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- name: to_predict |
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dtype: float32 |
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- name: timeseries |
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sequence: |
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sequence: float32 |
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- name: item_id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1325820 |
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num_examples: 95 |
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download_size: 686384 |
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dataset_size: 1325820 |
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license: gpl-3.0 |
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task_categories: |
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- time-series-forecasting |
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language: |
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- en |
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pretty_name: Appliances Energy Regression Dataset |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset Card for Time Series Extrinsic Regression |
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## Dataset Description |
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- **Homepage:** [Time Series Extrinsic Regression Repository](http://tseregression.org/) |
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- **Repository:** [GitHub code repository](https://github.com/ChangWeiTan/TS-Extrinsic-Regression/tree/master), [Raw data repository](https://zenodo.org/record/3902651) |
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- **Paper:** [Monash University, UEA, UCR Time Series Extrinsic Regression Archive](https://arxiv.org/abs/2006.10996) |
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- **Leaderboard:** [Baseline results](http://tseregression.org/#results) |
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- **Point of Contact:** [Stephen Fox]([email protected]) |
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### Dataset Summary |
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A collection of datasets from Monash, UEA, and UCR supporting research into Time Series Extrinsic Regression (TSER), |
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a regression task of which the aim is to learn the relationship between *a time series and a continuous scalar variable*. |
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This task is closely related to time series classification, where a single categorical variable is learned. |
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Please read the [paper](https://arxiv.org/abs/2006.10996) for more. |
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If you use the results or code, please cite the paper |
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**"Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb, Time Series Extrinsic Regression: Predicting numeric values from time series data"**. |
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(Full BibTex citation can be found at the end of this card). |
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(It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).) |
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### Supported Tasks and Leaderboards |
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[More Information Needed] |
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### Languages |
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## Dataset Structure |
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### Data Instances |
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A sample from the training set of Appliances Energy (a multivariate time series dataset) is provided. |
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The following is a single record from that dataset: |
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```python |
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{'start': Timestamp('2016-02-28 17:00:00'), |
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'feat_static_cat': 0, |
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'to_predict': 19.38, |
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'timeseries': array([[21.29 , 21.29 , 21.29 , ..., 21.79 , |
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21.79 , 21.79 ], |
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[31.66666667, 31.92666667, 32.06 , ..., 33.66 , |
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33.7 , 33.56666667], |
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[19.89 , 19.82333333, 19.79 , ..., 19.79 , |
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19.79 , 19.79 ], |
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..., |
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[ 7. , 6.83333333, 6.66666667, ..., 5. , |
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5. , 5. ], |
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[40. , 40. , 40. , ..., 40. , |
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40. , 40. ], |
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[-4.2 , -4.16666667, -4.13333333, ..., -4.3 , |
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-4.16666667, -4.03333333]]), |
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'item_id': 'item_000'} |
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``` |
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### Data Fields |
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This format was loosely adapted from [the Gluon format](https://ts.gluon.ai/stable/getting_started/concepts.html) |
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and [the HF convention](https://github.com/huggingface/notebooks/blob/main/examples/time_series_datasets.ipynb) |
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also seen in the recent [series](https://huggingface.co./blog/time-series-transformers) of [Time Series Transformer notebooks](https://github.com/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) |
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- `start`: a datetime of the first entry of each time series in the data record |
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- `feat_static_cat`: the original identifier given to this record |
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- `timeseries`: the timeseries itself |
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- `to_predict`: continuous variable to predict |
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- `item_id`: an identifier given to each record (for e.g. group-by style aggregations) |
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The `timeseries` field will be a single array in the univariate forecasting scenario, and a 2-D array in the multivariate scenario. |
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The `to_predict` will be a single number in most cases, or an array in a few instances (noted in the table above **TODO**). |
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### Data Splits |
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Train and test are temporally split (i.e. "train" is the past and "test" is the future) 70/30 whenever possible, though some datasets have more particular splits. |
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For details, see [the paper](https://arxiv.org/abs/2006.10996) and the particular dataset you are interested in. In our porting to HF Hub, we made as few changes as possible. |
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## Dataset Creation |
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While I (Stephen) did not create the original dataset, I took the initiative to put the data on Hugging Face Hub. |
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**Any grievances with the dataset should first and foremost be directed to me**. |
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### Curation Rationale |
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To facilitate the evaluation of global forecasting models that are predicting a single-point estimate in the future. |
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All datasets in the repository are intended for research purposes and to evaluate the performance of new TSER algorithms. |
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This |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The origins of each dataset are articulated in [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9). |
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Minimal preprocess was applied to the dataset, as they are still in their `sktime`-compatible `.ts` format. (As far as Stephen is aware.) |
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#### Who are the source language producers? |
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The data comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results) |
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### Annotations |
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#### Annotation process |
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Please see [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9) for the annotation aggregation propcess |
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#### Who are the annotators? |
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The annotation comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results) |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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- [Chang Wei Tan](https://changweitan.com/) |
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- [Anthony Bagnall](https://www.uea.ac.uk/computing/people/profile/anthony-bagnall) |
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- [Christoph Bergmeir](https://research.monash.edu/en/persons/christoph-bergmeir) |
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- [Daniel Schmidt](https://research.monash.edu/en/persons/daniel-schmidt) |
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- [Eamonn Keogh](http://www.cs.ucr.edu/~eamonn/) |
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- [François Petitjean](https://www.francois-petitjean.com/) |
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- [Geoff Webb](http://i.giwebb.com/) |
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### Licensing Information |
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[GNU General Public License (GPL) 3](https://www.gnu.org/licenses/gpl-3.0.en.html) |
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### Citation Information |
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```tex |
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@article{ |
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Tan2020TSER, |
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title={Time Series Extrinsic Regression}, |
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author={Tan, Chang Wei and Bergmeir, Christoph and Petitjean, Francois and Webb, Geoffrey I}, |
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journal={Data Mining and Knowledge Discovery}, |
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pages={1--29}, |
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year={2021}, |
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publisher={Springer}, |
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doi={https://doi.org/10.1007/s10618-021-00745-9} |
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} |
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``` |
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### Contributions |
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[More Information Needed] |
