TSExplain: Explaining Aggregated Time Series by Surfacing Evolving Contributors

TSExplain helps you find the evolving explanations of the aggregated time series!

Aggregated time series are generated effortlessly everywhere, e.g., "total confirmed covid-19 cases since 2019" and "total liquor sales over time." Understanding "how" and "why" these key performance indicators (KPI) evolve over time is critical to making data-informed decisions. Existing explanation engines focus on explaining one aggregated value or the difference between two relations. However, this falls short of explaining KPIs' continuous changes over time. TSEXPLAIN is a system that explains aggregated time series by surfacing the underlying evolving top contributors.

You can find related full paper published at ICDE 2023 and the demo paper published at SIGMOD.

Project page

Demo

tsexplain.mp4

Compile TSExplain

mkdir build
cd build
cmake ..
make

Install Python dependencies

pip install matplotlib streamlit

Run the demo interface

cd demo 

streamlit run demo.py

Citation

@article{chen2022tsexplain,
  title={TSEXPLAIN: Explaining Aggregated Time Series by Surfacing Evolving Contributors},
  author={Chen, Yiru and Huang, Silu},
  journal={arXiv preprint arXiv:2211.10909},
  year={2022}
}

@inproceedings{chen2021tsexplain,
  title={Tsexplain: Surfacing evolving explanations for time series},
  author={Chen, Yiru and Huang, Silu},
  booktitle={Proceedings of the 2021 International Conference on Management of Data},
  pages={2686--2690},
  year={2021}
}