PyPOTS: A Python Toolkit for Machine Learning on Partially-Observed Time Series
It addresses the problem of handling missing values in time series data for researchers and practitioners, offering a well-documented and scalable solution, though it is incremental as it packages existing methods.
PyPOTS is an open-source Python toolkit for machine learning on partially-observed time series, providing a unified library with algorithms for imputation, forecasting, anomaly detection, classification, and clustering to facilitate research and applications.
PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series with missing values. Particularly, it provides easy access to diverse algorithms categorized into five tasks: imputation, forecasting, anomaly detection, classification, and clustering. The included models represent a diverse set of methodological paradigms, offering a unified and well-documented interface suitable for both academic research and practical applications. With robustness and scalability in its design philosophy, best practices of software construction, for example, unit testing, continuous integration and continuous delivery, code coverage, maintainability evaluation, interactive tutorials, and parallelization, are carried out as principles during the development of PyPOTS. The toolbox is available on PyPI, Anaconda, and Docker. PyPOTS is open source and publicly available on GitHub https://github.com/WenjieDu/PyPOTS.