a Python toolbox for benchmarking ML on POTS (Partially-Observed Time Series)

To evaluate the performance of algorithms on POTS datasets, a benchmarking toolkit is necessary, hence the ecosystem library BenchPOTS is developed. BenchPOTS provides the standard and unified preprocessing pipelines of a variety of POTS datasets. It supports a variety of evaluation tasks to help users understand the performance of different algorithms.

The paper introducing PyPOTS project is available on arXiv at this URL, and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for Machine Learning Open Source Software). If you use BenchPOTS in your work, please cite PyPOTS project as below and 🌟star this repository to make others notice this library. 🤗 Thank you!

@article{du2023pypots,
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
year={2023},
eprint={2305.18811},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2305.18811},
doi={10.48550/arXiv.2305.18811},
}

or

Wenjie Du. (2023). PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811

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