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PyPOTS
A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science tasks of imputation, classification, clustering, and forecasting on incomplete (irregularly-sampled) multivariate time series with NaN missing values/data.
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BrewPOTS reviews and mentions
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We're building 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. 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, questions, and contributions are all very welcome! Website: https://pypots.com Paper link: https://arxiv.org/abs/2305.18811 GitHub repo: https://github.com/WenjieDu/PyPOTS Tutorials: https://github.com/WenjieDu/BrewPOTS Docs: https://docs.pypots.com
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We're building PyPOTS: an open-source Python toolbox for data mining on Partially-Observed Time Series
Tutorials: https://github.com/WenjieDu/BrewPOTS
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WenjieDu/BrewPOTS is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of BrewPOTS is Jupyter Notebook.
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