A comprehensive implementation of dynamic time warping (DTW) algorithms in Python. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.
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Summary
A comprehensive implementation of dynamic time warping (DTW) algorithms in Python. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.
Last Updated
Dec 13, 2025 at 18:38
License
GPL-3.0-or-later
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0
GitHub Repository
https://github.com/DynamicTimeWarping/dtw-pythonDocumentation
https://dynamictimewarping.github.io/python/