Welcome to the Dynamic Time Warp project!

Comprehensive implementation of Dynamic Time Warping algorithms in R and Python. 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 dtw-python module on PyPi is its direct Python equivalent.

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.


DTW is a family of algorithms which compute the local stretch or compression to apply to the time axes of two timeseries in order to optimally map one (query) onto the other (reference). DTW outputs the remaining cumulative distance between the two and, if desired, the mapping itself (warping function). DTW is widely used e.g. for classification and clustering tasks in econometrics, chemometrics and general timeseries mining.

The implementation in dtw provides:

Multivariate timeseries can be aligned with arbitrary local distance definitions, leveraging the proxy::dist (R) or scipy.spatial.distance.cdist (Python) functions.

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.

The R and Python pages contain links to programming language-specific documentation.


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:

When using partial matching (unconstrained endpoints via the open.begin/open.end options) and/or normalization strategies, please also cite:

See a gallery of sample plots, straight out of the examples in the documentation.


Both are available for all major platforms.


Ready-to-try examples are available in the DTW for R and DTW for Python pages.


This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.


Toni dot Giorgino at gmail.com

Istituto di Biofisica (IBF-CNR)
Consiglio Nazionale delle Ricerche
c/o Dept. of Biosciences, University of Milan
Milano, Italy

Academic and public research institutions are welcome to invite me for discussions or seminars. Please indicate dates, preferred format, and audience type.

Commercial support

I am also interested in hearing from companies seeking to use DTW in a commercial setting. Companies may contract on-site and remote research and development on DTW-based projects through the Biophysics Institute.