README.md

---
license: other
license_name: other
license_link: LICENSE
---

# 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](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/air_quality/LICENSE)   |
|Airlines                                  |                 CC0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/airlines/LICENSE)   |
|Appliances Energy Prediction              |           CC BY 4.0|   [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/appliances_energy/LICENSE)  |
|Beijing PM2.5                             |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/beijing_pm25/LICENSE)   |
|Bike Sharing (Washington DC)              |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/bike_sharing/LICENSE)   |
|CMAPSS                                    |       Public Domain|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/CMAPSS/LICENSE)   |
|Coffee Distribution                       |       Public Domain|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/coffee_distribution/LICENSE)   |
|Gas Turbine Emission                      |           CC BY 4.0|   [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/gas_turbine_emission/LICENSE)  |
|Marine Cargo Vessel Power Consumption     |    CC BY-NC-SA 4.0 |   [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/marine_cargo_vessel_power/LICENSE)  |
|Metro Interstate Traffic Volume           |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/metro_interstate_traffic/LICENSE)   |
|Miami Housing 2016                        |    CC BY-NC-SA 4.0 |  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/miami_housing/LICENSE)   |
|NOAA Weather                              |                 CC0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/NOAA_weather/LICENSE)   |
|Parking Birmingham                        |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/parking_birmingham/LICENSE)   |
|Parkinsons Telemonitoring                 |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/parkinsons_telemonitoring/LICENSE)   |
|Seoul Bike Sharing Demand                 |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/seoul_bike_sharing/LICENSE)   |
|Shifts Weather                            |    CC BY-NC-SA 4.0 |  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/shifts_weather/LICENSE)   |
|Steel Industry Energy Consumption         |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/steel_industry_energy_consumption/LICENSE)   |
|Temperature Forecast                      |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/temperature_forecast/LICENSE)   |
|Tetouan City Power Consumption            |           CC BY 4.0|  [Link](https://huggingface.co/datasets/yungak/TOTRCD/blob/main/LICENSE/tetouan_city_power/LICENSE)   |



## 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}
}
```