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Welcome to Dynamic Time Warp project!

Comprehensive implementation of Dynamic Time Warping algorithms in R. Supports arbitrary local (eg symmetric, asymmetric, slope-limited) and global (windowing) constraints, fast native code, several plot styles, and more.

The R Package dtw provides the most complete, freely-available (GPL) implementation of Dynamic Time Warping-type (DTW) algorithms up to date.

The package is described in a companion paper, including detailed instructions and extensive background on things like multivariate matching, open-end variants for real-time use, interplay between recursion types and length normalization, history, etc.


The basic DTW algorithm computes the time axis stretch which optimally maps one timeseries (query) onto another (reference); it outputs the remaining cumulative distance between the two. DTW is widely used e.g. for classification and clustering tasks in econometrics, chemometrics and general timeseries mining.

The R implementation in dtw provides:

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

In addition to computing alignments, the package provides:


The best place to learn how to use the package (and a hopefully a decent deal of background on DTW) is the companion paper Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package, which the Journal of Statistical Software makes available for free.

To have a look at how the dtw package is used in domains ranging from bioinformatics to chemistry to data mining, have a look at the list of citing papers.


If you use dtw, do cite it in any publication reporting results obtained with this software. Please follow the directions given in citation("dtw"), i.e. cite:

Toni Giorgino (2009). Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24. URL www.jstatsoft.org/v31/i07/.

When using partial matching (unconstrained endpoints) and/or normalization strategies, please also cite:

Paolo Tormene, Toni Giorgino, Silvana Quaglini, Mario Stefanelli (2008). Matching Incomplete Time Series with Dynamic Time Warping: An Algorithm and an Application to Post-Stroke Rehabilitation. Artificial Intelligence in Medicine, 45(1), 11-34. doi:10.1016/j.artmed.2008.11.007

Plot gallery

Go to a gallery of sample plots (straight out of the examples in the documentation).

Quickstart Example

## A noisy sine wave as query

## A cosine is for template; sin and cos are offset by 25 samples

## Find the best match with the canonical recursion formula

## Display the warping curve, i.e. the alignment curve

## Align and plot with the Rabiner-Juang type VI-c unsmoothed recursion

## See the recursion relation, as a figure and text

## And much more!


To install the latest stable build of the package (hosted at CRAN), issue the following command in the R console (automated installs require R version > 2.6):
> install.packages("dtw");

To get started, begin from the installed documentation:
> library(dtw)
> ?dtw
> ?plot.dtw

You are also welcome to test the development version, hosted at R-forge (project summary page):
> install.packages("dtw",repos="http://r-forge.r-project.org")

Frequently asked questions

I've discovered a multidimensional/multivariate version of the DTW algorithm! Shall it be included in the package?

Alas, most likely you haven't. DTW had been "multidimensional" since its conception. Local distances are computed between N-dimensional vectors; feature vectors have been extensively used in speech recognition since the '70s (see e.g. things like MFCC, RASTA, cepstrum, etc). Don't worry: several other people have "rediscovered" multivariate DTW already. The dtw package supports the numerous types of multi-dimensional local distances that the proxy package does, as explained in section 3.6 of the paper in JSS.

I've discovered a realtime/early detection version of the DTW algorithm!

Alas, most likely you haven't. A natural solution for real-time recognition of timeseries is "unconstrained DTW", which relaxes one or both endpoint boundary conditions. To my knowledge, the algorithm was published as early as 1978 by Rabiner, Rosenberg, and Levinson under the name UE2-1: see e.g. the mini-review in (Tormene and Giorgino, 2008). Feel also free to learn about the clever algorithms or expositions by Sakurai et al. (2007); Latecki (2007); Mori et al. (2006); Smith-Waterman (1981); Rabiner and Schmidt (1980); etc. Open-ended alignments (at one or both ends) are available in the dtw package, as described in section 3.5 of the JSS paper.

Can I use dtw in Python?

Yes. See Stefan Novak's version of the quickstart example on Stack Overflow. The mapping is performed through the Python package rpy2, which makes the code natural and readable. It also plays well with numpy and multiprocessing.

What about derivative dynamic time warping?

See command diff.


This software is distributed under the terms of the GNU General Public License Version 2, June 1991. The terms of this license are in a file called COPYING which you should have received with this software and which can be displayed by RShowDoc("COPYING").


Toni Giorgino at gmail.com

Istituto di Ingegneria Biomedica (ISIB-CNR)
Consiglio Nazionale delle Ricerche
Padova, Italy


Training. Academic institutions can request a personalized introduction to dtw. Please indicate dates, audience type and preferred length.

Support. On-site and remote consultancy is available through the Institute of Biomedical Engineering.

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