
# 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]
task_categories:time-series-forecasting size_categories:10K<n<100K language:en license:gpl-3.0 arxiv:2006.10996 region:us
{"language": ["en"], "license": "gpl-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["time-series-forecasting"], "pretty_name": "Appliances Energy Regression Dataset", "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}}
TAGS
#task_categories-time-series-forecasting #size_categories-10K<n<100K #language-English #license-gpl-3.0 #arxiv-2006.10996 #region-us 


# Dataset Card for Time Series Extrinsic Regression

## Dataset Description

- Homepage: Time Series Extrinsic Regression Repository
- Repository: GitHub code repository, Raw data repository
- Paper: Monash University, UEA, UCR Time Series Extrinsic Regression Archive
- Leaderboard: Baseline results
- Point of Contact: Stephen Fox

### 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 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.)

### Supported Tasks and Leaderboards



### 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:



### Data Fields
This format was loosely adapted from the Gluon format
and the HF convention
also seen in the recent series of Time Series Transformer notebooks

- '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 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.

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

### Annotations

#### Annotation process

Please see the paper 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

### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators

- Chang Wei Tan
- Anthony Bagnall
- Christoph Bergmeir
- Daniel Schmidt
- Eamonn Keogh
- François Petitjean
- Geoff Webb

### Licensing Information

GNU General Public License (GPL) 3




### Contributions


# Dataset Card for Time Series Extrinsic Regression ## Dataset Description

- Homepage: Time Series Extrinsic Regression Repository
- Repository: GitHub code repository, Raw data repository
- Paper: Monash University, UEA, UCR Time Series Extrinsic Regression Archive
- Leaderboard: Baseline results
- Point of Contact: Stephen Fox ### 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 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.) ### Supported Tasks and Leaderboards ### 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: ### Data Fields
This format was loosely adapted from the Gluon format
and the HF convention
also seen in the recent series of Time Series Transformer notebooks

- '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 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.

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 ### Annotations #### Annotation process

Please see the paper 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 ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

- Chang Wei Tan
- Anthony Bagnall
- Christoph Bergmeir
- Daniel Schmidt
- Eamonn Keogh
- François Petitjean
- Geoff Webb ### Licensing Information

GNU General Public License (GPL) 3 ### Contributions
TAGS
#task_categories-time-series-forecasting #size_categories-10K<n<100K #language-English #license-gpl-3.0 #arxiv-2006.10996 #region-us 
 # Dataset Card for Time Series Extrinsic Regression ## Dataset Description

- Homepage: Time Series Extrinsic Regression Repository
- Repository: GitHub code repository, Raw data repository
- Paper: Monash University, UEA, UCR Time Series Extrinsic Regression Archive
- Leaderboard: Baseline results
- Point of Contact: Stephen Fox ### 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 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.) ### Supported Tasks and Leaderboards ### 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: ### Data Fields
This format was loosely adapted from the Gluon format
and the HF convention
also seen in the recent series of Time Series Transformer notebooks

- '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 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.

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 ### Annotations #### Annotation process

Please see the paper 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 ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

- Chang Wei Tan
- Anthony Bagnall
- Christoph Bergmeir
- Daniel Schmidt
- Eamonn Keogh
- François Petitjean
- Geoff Webb ### Licensing Information

GNU General Public License (GPL) 3 ### Contributions
passage: TAGS
#task_categories-time-series-forecasting #size_categories-10K<n<100K #language-English #license-gpl-3.0 #arxiv-2006.10996 #region-us 
# Dataset Card for Time Series Extrinsic Regression## Dataset Description

- Homepage: Time Series Extrinsic Regression Repository
- Repository: GitHub code repository, Raw data repository
- Paper: Monash University, UEA, UCR Time Series Extrinsic Regression Archive
- Leaderboard: Baseline results
- Point of Contact: Stephen Fox### 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 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.)### Supported Tasks and Leaderboards### 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: