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Wenjie Duleft a comment
Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This 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...
PyPOTSa Python lib for data mining on PartiallyObserved TimeSeries
PyPOTS is the first (and so far the only) Python toolbox/library specifically designed 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.
PyPOTSa Python lib for data mining on PartiallyObserved TimeSeries