Comprehensive implementation of Dynamic Time Warping (DTW) algorithms in R.

The DTW algorithm computes the stretch of the time axis which optimally maps one given timeseries (query) onto whole or part of another (reference). It yields the remaining cumulative distance after the alignment and the point-by-point correspondence (warping function). DTW is widely used e.g. for classification and clustering tasks in econometrics, chemometrics and general timeseries mining.

package, ts

Please see documentation for function dtw(), which is the main entry point to the package.

The R implementation in dtw provides:

  • arbitrary windowing functions (global constraints), eg. the Sakoe-Chiba band; see dtwWindowingFunctions()

  • arbitrary transition types (also known as step patterns, slope constraints, local constraints, or DP-recursion rules). This includes dozens of well-known types; see stepPattern():

    • all step patterns classified by Rabiner-Juang, Sakoe-Chiba, and Rabiner-Myers;

    • symmetric and asymmetric;

    • Rabiner's smoothed variants;

    • arbitrary, user-defined slope constraints

  • partial matches: open-begin, open-end, substring matches

  • proper, pattern-dependent, normalization (exact average distance per step)

  • the Minimum Variance Matching (MVM) algorithm (Latecki et al.)

Multivariate timeseries can be aligned with arbitrary local distance definitions, leveraging the proxy::dist() function of package proxy. DTW itself becomes a distance function with the dist semantics.

In addition to computing alignments, the package provides:

  • methods for plotting alignments and warping functions in several classic styles (see plot gallery);

  • graphical representation of step patterns;

  • functions for applying a warping function, either direct or inverse; and more.

If you use this software, please cite it according to citation("dtw"). The package home page is at http://dtw.r-forge.r-project.org.


  • Toni Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24. http://www.jstatsoft.org/v31/i07/

  • Tormene, P.; Giorgino, T.; Quaglini, S. & Stefanelli, M. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med, 2009, 45, 11-34

  • Rabiner, L. R., & Juang, B.-H. (1993). Chapter 4 in Fundamentals of speech recognition. Englewood Cliffs, NJ: Prentice Hall.

See Also

dtw() for the main entry point to the package; dtwWindowingFunctions() for global constraints; stepPattern() for local constraints; analogue::distance(), outer() for building a local cost matrix with multivariate timeseries and custom distance functions.

  • dtw-package
 ## demo(dtw);

# }
Documentation reproduced from package dtw, version 1.21-3, License: GPL (>= 2)

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