Datasets:

foxy-steve
/

monash_uea_ucr_tser

	---
	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]