We built PyPOTS, an open-source toolbox for data mining on partially-observed time series

PyPOTS is a Python toolbox/library for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets.

Feedback and contributions are very welcome!

Website: [https://pypots.com](https://pypots.com/)

GitHub repo: [https://github.com/WenjieDu/PyPOTS](https://github.com/WenjieDu/PyPOTS)

Paper link: [https://arxiv.org/abs/2305.18811](https://arxiv.org/abs/2305.18811)

https://preview.redd.it/gstk0nutpz5b1.jpg?width=1801&format=pjpg&auto=webp&v=enabled&s=7b39cdb94d6fa0793de93d731f2573790c993715

classification clustering data data mining datascience datasets feedback forecasting imputation library machine machine learning mining missing values python series time series values