dtw-package: Dynamic Time Warp algorithms in R

Description

Dynamic Time Warp: find the optimal alignment between two time series.

Details

Package: dtw
Type: Package
Version: 1.15
Date: 2012-8-22
License: GPL-2
URL: http://dtw.r-forge.r-project.org

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.

To get the latest stable version from CRAN, use install.packages("dtw"). To get the development version (possibly unstable), use install.packages("dtw",repos="http://r-forge.r-project.org").

Author(s)

Toni Giorgino, Copyright (c) 2007-2013
Istituto di Ingegneria Biomedica (ISIB-CNR)
National Research Council of Italy

Maintainer: toni.giorgino@gmail.com

References

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.

Examples

1
2
 library(dtw);
 ## demo(dtw);

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.