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.

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 lacks a dedicated toolkit. PyPOTS is created to fill in this blank. PyPOTS (pronounced "Pie Pots") 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, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets. It is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS has unified APIs together with detailed documentation and interactive examples across algorithms as tutorials. Feedback and contributions are very welcome! Website: https://pypots.com Paper link: https://arxiv.org/abs/2305.18811 GitHub repo: https://github.com/WenjieDu/PyPOTS

Love the name and cute logo! What projects have you used this for? Anything we can check out?

@naomichao Thanks, Naomi! PyPOTS has already been used in research projects. There're scientific research projects using PyPOTS and referencing in their papers. Here is an incomplete list of them https://scholar.google.com/schol... You can also find PyPOTS tutorials at https://github.com/WenjieDu/Brew..., where we apply all models in PyPOTS to do imputation/classification/clustering/forecasting tasks on the dataset PhysioNet-2012, a widely used dataset collected from ICU patients.

@wdu Wow! Super cool. I might end up using this for my senior project analysis, so thanks!

@naomichao Awesome! Let me know if you have any questions regarding PyPOTS.

Congratulations on your launch Wenjie 🤝

@luthando007 Many thanks, Luthando! Will build it better.

PyPOTS

a Python lib for data mining on PartiallyObserved TimeSeries

a Python lib for data mining on PartiallyObserved TimeSeries

PyPOTS

a Python lib for data mining on PartiallyObserved TimeSeries

a Python lib for data mining on PartiallyObserved TimeSeries

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