---
dataset_info:
  features:
  - name: start
    dtype: timestamp[s]
  - name: feat_static_cat
    dtype: uint64
  - name: to_predict
    dtype: float32
  - name: timeseries
    sequence:
      sequence: float32
  - name: item_id
    dtype: string
  splits:
  - name: train
    num_bytes: 1325820
    num_examples: 95
  - name: test
    num_bytes: 586152
    num_examples: 42
  download_size: 1020749
  dataset_size: 1911972
license: gpl-3.0
task_categories:
- time-series-forecasting
language:
- en
pretty_name: Appliances Energy Regression Dataset
size_categories:
- 10K<n<100K
---

# Dataset Card for Time Series Extrinsic Regression

## Dataset Description

- **Homepage:** [Time Series Extrinsic Regression Repository](http://tseregression.org/)
- **Repository:** [GitHub code repository](https://github.com/ChangWeiTan/TS-Extrinsic-Regression/tree/master), [Raw data repository](https://zenodo.org/record/3902651)
- **Paper:** [Monash University, UEA, UCR Time Series Extrinsic Regression Archive](https://arxiv.org/abs/2006.10996)
- **Leaderboard:** [Baseline results](http://tseregression.org/#results)
- **Point of Contact:** [Stephen Fox](gh@stephenjfox.com)

### Dataset Summary

A collection of datasets from Monash, UEA, and UCR supporting research into Time Series Extrinsic Regression (TSER),
a regression task of which the aim is to learn the relationship between *a time series and a continuous scalar variable*.
This task is closely related to time series classification, where a single categorical variable is learned.
Please read the [paper](https://arxiv.org/abs/2006.10996) for more.

If you use the results or code, please cite the paper
**"Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb, Time Series Extrinsic Regression: Predicting numeric values from time series data"**.
(Full BibTex citation can be found at the end of this card).

(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).)

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

## Dataset Structure

### Data Instances

A sample from the training set of Appliances Energy (a multivariate time series dataset) is provided.
The following is a single record from that dataset:

```python
{'start': Timestamp('2016-02-28 17:00:00'),
 'feat_static_cat': 0,
 'to_predict': 19.38,
 'timeseries': array([[21.29      , 21.29      , 21.29      , ..., 21.79      ,
         21.79      , 21.79      ],
        [31.66666667, 31.92666667, 32.06      , ..., 33.66      ,
         33.7       , 33.56666667],
        [19.89      , 19.82333333, 19.79      , ..., 19.79      ,
         19.79      , 19.79      ],
        ...,
        [ 7.        ,  6.83333333,  6.66666667, ...,  5.        ,
          5.        ,  5.        ],
        [40.        , 40.        , 40.        , ..., 40.        ,
         40.        , 40.        ],
        [-4.2       , -4.16666667, -4.13333333, ..., -4.3       ,
         -4.16666667, -4.03333333]]),
 'item_id': 'item_000'}
```

### Data Fields
This format was loosely adapted from [the Gluon format](https://ts.gluon.ai/stable/getting_started/concepts.html)
and [the HF convention](https://github.com/huggingface/notebooks/blob/main/examples/time_series_datasets.ipynb)
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)

- `start`: a datetime of the first entry of each time series in the data record
- `feat_static_cat`: the original identifier given to this record
- `timeseries`: the timeseries itself
- `to_predict`: continuous variable to predict
- `item_id`: an identifier given to each record (for e.g. group-by style aggregations)

The `timeseries` field will be a single array in the univariate forecasting scenario, and a 2-D array in the multivariate scenario.

The `to_predict` will be a single number in most cases, or an array in a few instances (noted in the table above **TODO**).

### Data Splits

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.

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.


## Dataset Creation

While I (Stephen) did not create the original dataset, I took the initiative to put the data on Hugging Face Hub.
**Any grievances with the dataset should first and foremost be directed to me**.

### Curation Rationale

To facilitate the evaluation of global forecasting models that are predicting a single-point estimate in the future.
All datasets in the repository are intended for research purposes and to evaluate the performance of new TSER algorithms.
This 

### Source Data

#### Initial Data Collection and Normalization

The origins of each dataset are articulated in [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9).

Minimal preprocess was applied to the dataset, as they are still in their `sktime`-compatible `.ts` format. (As far as Stephen is aware.)

#### Who are the source language producers?

The data comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)

### Annotations

#### Annotation process

Please see [the paper](https://link.springer.com/article/10.1007/s10618-021-00745-9) for the annotation aggregation propcess

#### Who are the annotators?

The annotation comes from the datasets listed in the paper and in the table on [the website](http://tseregression.org/#results)

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

- [Chang Wei Tan](https://changweitan.com/)
- [Anthony Bagnall](https://www.uea.ac.uk/computing/people/profile/anthony-bagnall)
- [Christoph Bergmeir](https://research.monash.edu/en/persons/christoph-bergmeir)
- [Daniel Schmidt](https://research.monash.edu/en/persons/daniel-schmidt)
- [Eamonn Keogh](http://www.cs.ucr.edu/~eamonn/)
- [François Petitjean](https://www.francois-petitjean.com/)
- [Geoff Webb](http://i.giwebb.com/)

### Licensing Information

[GNU General Public License (GPL) 3](https://www.gnu.org/licenses/gpl-3.0.en.html)

### Citation Information

```tex
@article{
  Tan2020TSER,
  title={Time Series Extrinsic Regression}, 
  author={Tan, Chang Wei and Bergmeir, Christoph and Petitjean, Francois and Webb, Geoffrey I},
  journal={Data Mining and Knowledge Discovery},
  pages={1--29},
  year={2021},
  publisher={Springer},
  doi={https://doi.org/10.1007/s10618-021-00745-9}
}

```
### Contributions

[More Information Needed]