PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time
  Series
Wenjie Du
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.