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Summary

This article lists open source implementations and notable applications of Dynamic Time Warping (DTW) identified in the literature, patents, and other resources. It is intended as a living document and will be updated periodically with new findings.

Open Source Implementations

This section lists several open source implementations of DTW in R and Python, for those who wish to experiment with DTW directly.

R

  • dtw, by Toni Giorgino: most complete DTW suite in R, with a companion paper here. It also has a Python implementation linked below.
  • dtwclust, by Alexis Sarda-Espinosa: focused on applying DTW for time series clustering. It has a very comprehensive technical vignette here.
  • IncDTW, by Maximilian Leodolter: efficient implementation with a companion paper, focused on fast DTW computations for real-time applications.

Python

  • dtw-python: Python implementation of dtw.
  • dtaidistance: fast C implementation of DTW, with a great documentation page here.
  • tslearn, by Romain Tavenard: focused on time series clustering via DTW for machine learning modeling.
  • fastdtw, by Stan Salvador and Philip Chan: Python implementation of FastDTW, a fast approximation to DTW described in this paper.

Other Languages

The Wikipedia page on Dynamic Time Warping lists implementations in other languages in the section Open Source Software.