[P] PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series

Due to all kinds of reasons like failures of collection sensors, communication errors, and unexpected malfunctions, missing values are common to see in time series from the real-world environment. No matter whether we like them or not, missing data makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this gap. …

collection communication data data mining environment errors machinelearning mining missing values python sensors series time series values world