dtw-package: Dynamic Time Warping Algorithms

Description Details Author(s) References See Also Examples


A comprehensive implementation of dynamic time warping (DTW) algorithms in R. 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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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.

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

The R implementation in dtw provides:

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

In addition to computing alignments, the package provides:

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 <[email protected]>,

Maintainer: Toni Giorgino <[email protected]>


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; distance, outer for building a local cost matrix with multivariate timeseries and custom distance functions.


 ## demo(dtw);

Example output

Loading required package: proxy

Attaching package: 'proxy'

The following objects are masked from 'package:stats':

    as.dist, dist

The following object is masked from 'package:base':


Loaded dtw v1.18-1. See ?dtw for help, citation("dtw") for use in publication.

dtw documentation built on May 18, 2018, 9:03 a.m.