PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
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.
Code (5)
Tasks
Anomaly DetectionClassificationClassification on Time Series with Missing DataClusteringImputationIrregular Time SeriesMissing ValuesMultivariate Time Series ImputationPhilosophyTime SeriesTime Series AnalysisTime Series Anomaly DetectionTime Series ClassificationTime Series ClusteringTime Series ForecastingTraffic Data ImputationMethods 이 논문이 사용한 방법론
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