Datasetpublic

ddrg/TOTRCD

TOTRCD: Temporally-Ordered Tabular Regression Benchmark Suite with Concept Drift TOTRCD is a collection of tabular regression datasets that include a temporal ordering, represented by a time column, and exhibit some form of concept drift. Dataset Structure Data Fields The columns of the datasets follows all a similar formatting: Meta::: prefix marking identifiers, timestamps, or sort keys that are not used as model inputs. Example: Meta::DateTime.… See the full description on the dataset page: https://huggingface.co/datasets/ddrg/TOTRCD.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

0likes204downloads

Dataset Card

TOTRCD: Temporally-Ordered Tabular Regression Benchmark Suite with Concept Drift

TOTRCD is a collection of tabular regression datasets that include a temporal ordering, represented by a time column, and exhibit some form of concept drift.

Dataset Structure

Data Fields

The columns of the datasets follows all a similar formatting:

—`Meta::`: prefix marking identifiers, timestamps, or sort keys that are not used as model inputs. Example: Meta::DateTime.
—`Covariate::Static::`: prefix marking time invariant input features. Example: Covariate::Static::Distance.
—`Covariate::Temporal::`: prefix marking cyclically encoded periodic input features, obtained from DateTimes. Example: Covariate::Temporal::Hour (sin).
—`Target::`: prefix marking the dependent variable(s) to be predicted. Example: Target::traffic_volume.
—`::dummy::`: infix inserted between a categorical variable's original name and its category value, marking a one hot encoded column. Example: Covariate::Static::weather::dummy::Rain is the one hot indicator for the category Rain of the original variable weather.

Dataset Sizes

Dataset	#Obs	#Feats	Mahalanobis	ASO	ADWIN
Air Quality	8,991 \	7,344 \	7,393 \	7,396	12	✅ \	✅ \	✅ \	✅	✅ \	✅ \	✅ \	✅	✅ \	✅ \	✅ \	✅
Airlines*	226,082,661	8	✅	❌	✅
Appliances Energy Prediction	19,735	29	✅	✅	✅
Beijing PM2.5	41,757	16	✅	✅	✅
Bike Sharing (Washington DC)	17,379	13	✅	✅	✅
CMAPSS	53,759 \	61,249	25	✅ \	✅	✅ \	✅	✅ \	✅
Coffee Distribution	6,016	103	✅	✅	✅
Gas Turbine Emission	36,733 \	36,733	9	✅ \	✅	✅ \	✅	✅ \	✅
Marine Cargo Vessel Power Consumption	567,442	10	✅	✅	✅
Metro Interstate Traffic Volume	47,942	22	✅	✅	✅
Miami Housing 2016	13,932	12	✅	✅	✅
NOAA Weather	19,515	12	✅	✅	✅
Parking Birmingham	35,705	35	✅	✅	✅
Parkinsons Telemonitoring	5,875 \	5,875	19	✅ \	✅	✅ \	✅	✅ \	✅
Seoul Bike Sharing Demand	8,465	18	✅	✅	✅
Shifts Weather	3,544,637	128	✅	❌	✅
Steel Industry Energy Consumption	35,040	15	✅	✅	✅
Temperature Forecast	7,588 \	7,588	46	✅ \	✅	✅ \	✅	✅ \	✅
Tetouan City Power Consumption	52,416 \	52,416 \	52,416	11	✅ \	✅ \	✅	✅ \	✅ \	✅	✅ \	✅ \	✅

Note: The vertical bar | separates different task (i.e. target labels) associated with the same dataset.

Dataset Creation

Curation Rationale

For inclusion in the suite, a dataset had to satisfy all of the following criteria:

1.Tabular regression task
2.Real world dataset, not synthetic
3.Includes a temporal column that induces a natural ordering
4.Publicly available under a license that permits redistribution
5.More than 5,000 observations
6.Fewer than 1,000 features after one hot encoding
7.Evidence of concept drift, confirmed by our drift detection pipeline

Licensing Information

Below the licenses of the used datasets can be found.

	License	Source & License
Air Quality	CC BY 4.0	Link
Airlines	CC0	Link
Appliances Energy Prediction	CC BY 4.0	Link
Beijing PM2.5	CC BY 4.0	Link
Bike Sharing (Washington DC)	CC BY 4.0	Link
CMAPSS	Public Domain	Link
Coffee Distribution	Public Domain	Link
Gas Turbine Emission	CC BY 4.0	Link
Marine Cargo Vessel Power Consumption	CC BY-NC-SA 4.0	Link
Metro Interstate Traffic Volume	CC BY 4.0	Link
Miami Housing 2016	CC BY-NC-SA 4.0	Link
NOAA Weather	CC0	Link
Parking Birmingham	CC BY 4.0	Link
Parkinsons Telemonitoring	CC BY 4.0	Link
Seoul Bike Sharing Demand	CC BY 4.0	Link
Shifts Weather	CC BY-NC-SA 4.0	Link
Steel Industry Energy Consumption	CC BY 4.0	Link
Temperature Forecast	CC BY 4.0	Link
Tetouan City Power Consumption	CC BY 4.0	Link

Citations

Below are the citations for the original publications that introduced the used datasets.

Air Quality

@article{Vito2008OnFC,
  title={On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario},
  author={Saverio De Vito and Ettore Massera and Marco Piga and Luca Martinotto and Girolamo Di Francia},
  journal={Sensors and Actuators B-chemical},
  year={2008},
  volume={129},
  pages={750-757},
  url={https://api.semanticscholar.org/CorpusID:94886265}
}

Appliances Energy Prediction

@article{Candanedo2017DataDP,
  title={Data driven prediction models of energy use of appliances in a low-energy house},
  author={Luis M. Ibarra Candanedo and Veronique Feldheim and Dominique Deramaix},
  journal={Energy and Buildings},
  year={2017},
  volume={140},
  pages={81-97},
  url={https://api.semanticscholar.org/CorpusID:63814994}
}

Beijing PM2.5

@article{Liang2015AssessingBP,
  title={Assessing Beijing's PM2.5 pollution: severity, weather impact, APEC and winter heating},
  author={Xuan Liang and Tao Zou and Bin Guo and Shuo Li and Haozhe Zhang and Shuyi Zhang and Hui Huang and Song Xi Chen},
  journal={Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  year={2015},
  volume={471},
  url={https://api.semanticscholar.org/CorpusID:130615236}
}

Bike Sharing (Washington DC)

@article{FanaeeT2013EventLC,
  title={Event labeling combining ensemble detectors and background knowledge},
  author={Hadi Fanaee-T and Jo{\~a}o Gama},
  journal={Progress in Artificial Intelligence},
  year={2013},
  volume={2},
  pages={113 - 127},
  url={https://api.semanticscholar.org/CorpusID:256282956}
}

Gas Turbine Emission

@article{Kaya2019PredictingCA,
  title={Predicting CO and NOx emissions from gas turbines: Novel data and a benchmark PEMS},
  author={Kaya, Heysem and T{\"u}fekci, P{\i}nar and Uzun, Erdin{\c{c}}},
  journal={Turkish Journal of Electrical Engineering and Computer Sciences},
  year={2019},
  volume={27},
  number={6},
  pages={4783--4796},
  doi={10.3906/elk-1807-87}
}

Marine Cargo Vessel Power Consumption

@dataset{malinin_2022_7684813,
  title={Shifts Marine Cargo Vessel Power Consumption Prediction Dataset},
  author={Malinin, Andrey and Athanasopoulos, Andreas and Barakovic, Muhamed and Bach Cuadra, Meritxell and Gales, Mark and Granziera, Cristina and Graziani, Mara and Kartashev, Nikolay and Kyriakopoulos, Konstantinos and Lu, Po-Jui and Molchanova, Nataliia and Nikitakis, Antonis and Raina, Vatsal and La Rosa, Francesco and Sivena, Eli and Tsarsitalidis, Vasileios and Tsompopoulou, Efi and Volf, Elena},
  publisher={Zenodo},
  month={sep},
  year={2022},
  version={2.0},
  doi={10.5281/zenodo.7684813},
  url={https://doi.org/10.5281/zenodo.7684813}
}

Metro Interstate Traffic Volume

@misc{metro_interstate_traffic_volume_492,
  author       = {Hogue, John},
  title        = {{Metro Interstate Traffic Volume}},
  year         = {2019},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C5X60B}
}

Miami Housing 2016

@techreport{Mayer2021StructuredAR,
  title={Structured Additive Regression and Tree Boosting},
  author={Mayer, Michael and Bourassa, Steven C. and Hoesli, Martin and Scognamiglio, Donato},
  institution={Swiss Finance Institute},
  type={Swiss Finance Institute Research Paper},
  number={21-83},
  year={2021},
  doi={10.2139/ssrn.3924412},
  url={https://ssrn.com/abstract=3924412}
}

Parking Birmingham

@inproceedings{Stolfi2017PredictingCP,
  title={Predicting Car Park Occupancy Rates in Smart Cities},
  author={Stolfi, Daniel H. and Alba, Enrique and Yao, Xin},
  booktitle={Smart Cities: Second International Conference, Smart-CT 2017},
  address={M{\'a}laga, Spain},
  pages={107--117},
  year={2017},
  doi={10.1007/978-3-319-59513-9_11}
}

Parkinsons Telemonitoring

@article{Tsanas2009AccurateTO,
  title={Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests},
  author={Athanasios Tsanas and Max A. Little and Patrick E. McSharry and Lorraine O. Ramig},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2009},
  volume={57},
  pages={884-893},
  url={https://api.semanticscholar.org/CorpusID:7382779}
}

Seoul Bike Sharing Demand

@article{Sathishkumar2020UsingDM,
  title={Using data mining techniques for bike sharing demand prediction in metropolitan city},
  author={Sathishkumar, V E and Park, Jangwoo and Cho, Yongyun},
  journal={Computer Communications},
  year={2020},
  volume={153},
  pages={353--366},
  doi={10.1016/j.comcom.2020.02.007}
}

@article{Sathishkumar2020ARB,
  title={A rule-based model for Seoul Bike sharing demand prediction using weather data},
  author={Sathishkumar, V E and Cho, Yongyun},
  journal={European Journal of Remote Sensing},
  year={2020},
  volume={53},
  number={sup1},
  pages={166--183},
  doi={10.1080/22797254.2020.1725789}
}

Shifts Weather

@inproceedings{
  malinin2021shifts,
  title={Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks},
  author={Andrey Malinin and Neil Band and Yarin Gal and Mark Gales and Alexander Ganshin and German Chesnokov and Alexey Noskov and Andrey Ploskonosov and Liudmila Prokhorenkova and Ivan Provilkov and Vatsal Raina and Vyas Raina and Denis Roginskiy and Mariya Shmatova and Panagiotis Tigas and Boris Yangel},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021},
  url={https://openreview.net/forum?id=qM45LHaWM6E}
}

Steel Industry Energy Consumption

@article{VE2020EfficientEC,
  title={Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city},
  author={Sathishkumar V E and Changsun Shin and Yongyun Cho},
  journal={Building Research \& Information},
  year={2020},
  volume={49},
  pages={127 - 143},
  url={https://api.semanticscholar.org/CorpusID:224916577}
}

Temperature Forecast

@misc{bias_correction_of_numerical_prediction_model_temperature_forecast_514,
  title        = {{Bias correction of numerical prediction model temperature forecast}},
  year         = {2020},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C59K76}
}

Tetouan City Power Consumption

@article{Salam2018ComparisonOM,
  title={Comparison of Machine Learning Algorithms for the Power Consumption Prediction : - Case Study of Tetouan city –},
  author={Abdul Rahim Salam and Abdelaaziz El Hibaoui},
  journal={2018 6th International Renewable and Sustainable Energy Conference (IRSEC)},
  year={2018},
  pages={1-5},
  url={https://api.semanticscholar.org/CorpusID:145050098}
}