Python library Stumpy releases its latest version 1.11.0
Sean Law, principal data scientist at Charles Schwab and the creator of Stumpy, announced the release of the latest version 1.11.0. The new version includes the oft-requested Minkowski (p-norm) Distance, support for Multi-dimensional Motif Discovery, new Annotation vector tutorials, and enhancements for Pan Matrix Profiles. The latest version is available on GitHub. Stumpy is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks such as: pattern/motif (approximately repeated subsequences within a longer time series) discoveryanomaly/novelty (discord) discoveryshapelet discoverysemantic segmentationstreaming (on-line) datafast approximate matrix profilestime series chains (temporally ordered set of subsequence patterns)snippets for summarising long time seriespan matrix profiles for selecting the best subsequence window size(s) With 30K+ downloads, over 1K+ Github stars, and 100% code coverage, Stumpy is a favourite of academics, data scientists, software developers, and time-series enthusiasts. Check out the latest documentation or explore the hands-on tutorials.
Stumpy is a powerful and scalable library for modern time series analysis.
Stumpy is a powerful and scalable library for modern time series analysis.
["Data Mining", "Python Library"]
["ELT", "programming_languages:R", "AI", "Data Mining", "Scala", "Git", "RAG", "programming_languages:Scala", "Python Library", "GitHub", "R"]
["AI", "RAG", "R", "Scala", "Git", "GitHub", "ELT", "programming_languages:R", "programming_languages:Scala"]
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