metadata

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
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

[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:

{'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 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

[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

Licensing Information

GNU General Public License (GPL) 3

Citation Information

@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]