PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.e. incomplete time series with missing values, A.K.A. irregularlysampled time series. Particularly, it provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting. The included models contain probabilistic approaches as well as neural-network methods, with a well-designed and fully-documented programming interface for both academic researchers and industrial professionals to use. With robustness and scalability in its design philosophy, best practices of software construction, for example, unit testing, continuous integration (CI) and continuous delivery (CD), code coverage, maintainability evaluation, interactive tutorials, and parallelization, are carried out as principles during the development of PyPOTS. The toolkit is available on both Python Package Index (PyPI) and Anaconda. PyPOTS is open-source and publicly available on GitHub https://github.com/WenjieDu/PyPOTS.

翻译：PyPOTS是一个开源的Python库，专为多元部分观测时间序列（即存在缺失值的不完整时间序列，又称不规则采样时间序列）的数据挖掘与分析而设计。该工具包提供了对四类任务的多样化算法接口：插补、分类、聚类与预测。包含的模型涵盖概率方法与神经网络方法，并配有设计精良、文档完善的编程接口，可供学术研究者与工业专业人员使用。PyPOTS的开发秉持鲁棒性与可扩展性的设计理念，严格遵循软件工程最佳实践原则，包括单元测试、持续集成/持续交付、代码覆盖率、可维护性评估、交互式教程及并行化处理。该工具包已发布至Python包索引（PyPI）与Anaconda平台，开源代码托管于GitHub仓库https://github.com/WenjieDu/PyPOTS。