\name{dtw-package}
\alias{dtw-package}
\docType{package}
\title{
\packageTitle{dtw}
}
\description{
\packageDescription{dtw}
}
\details{

The DESCRIPTION file:
\packageDESCRIPTION{dtw}
\packageIndices{dtw}

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 \code{\link{dtw}}, which is the
main entry point to the package.

The R implementation in dtw provides: 

\itemize{
\item    arbitrary windowing functions (global constraints), eg. the Sakoe-Chiba band; see \code{\link{dtwWindowingFunctions}}
\item    arbitrary transition types (also known as step patterns, slope constraints, local constraints, or DP-recursion rules). This includes dozens of well-known types; see \code{\link{stepPattern}}:
\itemize{
 \item        all step patterns classified by Rabiner-Juang, Sakoe-Chiba, and Rabiner-Myers;
 \item        symmetric and asymmetric;
 \item        Rabiner's smoothed variants;
 \item        arbitrary, user-defined slope constraints
}
\item    partial matches: open-begin, open-end, substring matches 
\item    proper, pattern-dependent, normalization (exact average distance per step)
\item    the Minimum Variance Matching (MVM) algorithm (Latecki et al.)
}

Multivariate timeseries can be aligned with arbitrary local distance
definitions, leveraging the \code{\link[proxy]{dist}} function of
package \pkg{proxy}. DTW itself becomes a distance function with the
dist semantics.

In addition to computing alignments, the package provides:
\itemize{
\item    methods for plotting alignments and warping functions in several classic styles (see plot gallery);
\item    graphical representation of step patterns;
\item    functions for applying a warping function, either direct or
inverse; and more.
}

If you use this software, please cite it according to
\code{citation("dtw")}.  The package home page is at
\url{http://dtw.r-forge.r-project.org}.


}
\author{
\packageAuthor{dtw}

Maintainer: \packageMaintainer{dtw}
}

\references{
  Toni Giorgino. \emph{Computing and Visualizing Dynamic Time Warping
        Alignments in R: The dtw Package.}  Journal of Statistical
        Software, 31(7), 1-24. \url{http://www.jstatsoft.org/v31/i07/}
\cr \cr
  Tormene, P.; Giorgino, T.; Quaglini, S. & Stefanelli,
    M. \emph{Matching incomplete time series with dynamic time warping: an
    algorithm and an application to post-stroke rehabilitation.} Artif
    Intell Med, 2009, 45, 11-34
\cr \cr
  Rabiner, L. R., & Juang, B.-H. (1993). Chapter 4 in
    \emph{Fundamentals of speech
    recognition.} Englewood Cliffs, NJ: Prentice Hall.
}


\keyword{ package }
\keyword{ ts }


\seealso{ \code{\link{dtw}} for the main entry point to the package;
  \code{\link{dtwWindowingFunctions}} for global constraints;
  \code{\link{stepPattern}} for local constraints;
  \code{\link[analogue]{distance}},  \code{\link{outer}} for
  building a local cost matrix with multivariate
  timeseries and custom distance functions. }
  
\examples{
 library(dtw);
 ## demo(dtw);
}