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:
- arbitrary windowing functions (global constraints), eg. the Sakoe-Chiba band and the Itakura parallelogram;
- arbitrary transition types (also known as step patterns, slope constraints, local constraints, or DP-recursion rules). This includes dozens of well-known types:
- 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.)
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;
- fast native (C) core.
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, doi:10.18637/jss.v031.i07.
When using partial matching (unconstrained endpoints via the
open.end options) and/or normalization strategies, please
- 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
See a gallery of sample plots, straight out of the examples in the documentation.
Both are available for all major platforms.
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
Academic and public research institutions are welcome to invite me for discussions or seminars. Please indicate dates, preferred format, and audience type.
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.