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.

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 R 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:
- all step patterns classified by Rabiner-Juang, Sakoe-Chiba, and Rabiner-Myers;
- symmetric and asymmetric;
- Rabiner's smoothed variants;
- arbitrary, user-defined slope constraints

- 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.)

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:

- 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;
- both fast native (C) and interpreted (R) cores.

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.

A link to prebuilt documentation is here.

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.begin`

/`open.end`

options) 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

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

```
## A noisy sine wave as query
idx<-seq(0,6.28,len=100);
query<-sin(idx)+runif(100)/10;
## A cosine is for template; sin and cos are offset by 25 samples
template<-cos(idx)
## Find the best match with the canonical recursion formula
library(dtw);
alignment<-dtw(query,template,keep=TRUE);
## Display the warping curve, i.e. the alignment curve
plot(alignment,type="threeway")
## Align and plot with the Rabiner-Juang type VI-c unsmoothed recursion
plot(
dtw(query,template,keep=TRUE,
step=rabinerJuangStepPattern(6,"c")),
type="twoway",offset=-2);
## See the recursion relation, as formula and diagram
rabinerJuangStepPattern(6,"c")
plot(rabinerJuangStepPattern(6,"c"))
## And much more!
```

To install the
latest stable
build of the package (hosted at CRAN), issue the following command
in the R console:

`> install.packages("dtw")`

To get started, begin from the installed documentation:

```
> library(dtw)
> demo(dtw)
> ?dtw
> ?plot.dtw
```

Alas, most likely you haven't. DTW had been "multidimensional" since its conception. Local distances are computed betweenN-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. Thedtwpackage 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.

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 thedtwpackage, as described in section 3.5 of the JSS paper.

Alas, most likely you haven't. Backtracking is not meant to be done via steepest descent. Here's a counterexample:The sum of costs along my warping path (blue) is (starting from [1,1]) 11+10+2*10+2*10+11 = 72 which is correct (=g[4,4]) . If you follow your backtracking "steepest descent" algorithm (red), you get the diagonal 11+2*11+2*11+2*11=77 which is wrong.`> library(dtw) > dm<-matrix(10,4,4)+diag(rep(1,4)) > al<-dtw(dm,k=T,step=symmetric2)`

> al$localCostMatrix [,1] [,2] [,3] [,4] [1,] 11 10 10 10 [2,] 10 11 10 10 [3,] 10 10 11 10 [4,] 10 10 10 11 > al$costMatrix [,1] [,2] [,3] [,4] [1,] 11 21 31 41 [2,] 21 32 41 51 [3,] 31 41 52 61 [4,] 41 51 61 72

`symmetric1`

recursion I found in Wikipedia/in another
implementation?An alignment computed with a non-normalizable step pattern has two serious drawbacks:This is discussed in section 3.2 of the JSS paper, section 4.2 of the AIIM paper, section 4.7 of Rabiner and Juang's Fundamentals of speech recognition book, and elsewhere. Make sure you familiarize yourself with those references.

- It cannot be meaningfully normalized by timeseries length. Hence, longer timeseries have naturally higher distances, in turn making comparisons impossible.
- It favors diagonal steps, therefore it is not robust: two paths differing for a small local change (eg. horizontal+vertical step rather than diagonal) have very different costs.

TLDR: just stick to the default`symmetric2`

recursion and use the value of`normalizedDistance`

.

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 reportedly plays well withnumpy,pandasandmultiprocessing. The following example has been updated for rpy2.`import numpy as np import rpy2.robjects.numpy2ri from rpy2.robjects.packages import importr rpy2.robjects.numpy2ri.activate() # Set up our R namespaces R = rpy2.robjects.r DTW = importr('dtw') # Generate our data idx = np.linspace(0, 2*np.pi, 100) template = np.cos(idx) query = np.sin(idx) + np.array(R.runif(100))/10 # Calculate the alignment vector and corresponding distance alignment = R.dtw(query, template, keep=True) dist = alignment.rx('distance')[0][0] print(dist)`

```
See
command
````diff`

.

This question has been raised on Stack Overflow; see here, here and here. A good first guess is`symmetric2`

(the default), i.e.`g[i,j] = min( g[i-1,j-1] + 2 * d[i ,j ] , g[i ,j-1] + d[i ,j ] , g[i-1,j ] + d[i ,j ] , )`

`dist`

and `dtw`

?

There are twovery different,totally unrelateduses for`dist`

. This is explained at length in the paper, but let's summarize.

- If you have
two multivariatetimeseries, you can feed them to`dist`

to obtain alocal distance matrix. You then pass this matrix to dtw(). This is equivalent to passing the two matrices to the dtw() function and specifying a`dist.method`

(see also the next question).- If you have
many univariatetimeseries, instead of iterating over all pairs and applying dtw() to each, you may feed the lot (arranged as a matrix) to`dist`

with`method="DTW"`

. In this case your code does NOT explicitly call dtw(). This is equivalent to iterating over all pairs; it is also equivalent to using the`dtwDist`

convenience function.

`dist.method`

appear to have no effect?

Because it only makes a difference when aligningmultivariatetimeseries. It specifies the "pointwise" or local distance used (before the alignment) between the query featurevectorat timei,`query[i,]`

and the reference featurevectorat timej,`ref[j,]`

. Most distance functions coincide with the Euclidean distance in the one-dimensional case. Note the following:`r<-matrix(runif(10),5) # A 2-D timeseries of length 5 s<-matrix(runif(10),5) # Ditto myMethod<-"Manhattan" # Or anything else al1<-dtw(r,s,dist.method=myMethod) # Passing the two inputs al2<-dtw(proxy::dist(r,s,method=myMethod)) # Equivalent, passing the distance matrix all.equal(al1,al2)`

```
The first thing you should try is to set
the
````distance.only=TRUE`

parameter, which skips
backtracing and some object copies. Second, consider
downsampling the input timeseries.

Of course. You need to start with a dissimilarity matrix, i.e. a matrix holding ini,jthe DTW distance between timeseriesiandj. This matrix is fed to the clustering functions. Obtaining the dissimilarity matrix is done differently depending on whether your timeseries are univariate or or multivariate: see the next questions.

Arrange the timeseries (single-variate) in a matrixas rows. Make sure you use a symmetric pattern. See dtwDist.

You have to handle the loop yourself. Assuming you have data arranged as`x[time,component,series]`

, pseudocode would be:`for (i in 1:N) { for (j in 1:N) { result[i,j] <- dtw( dist(x[,,i],x[,,j]), distance.only=T )$normalizedDistance`

Either loop over the inputs yourself, or pad with NAs and use the following code:`dtwOmitNA <-function (x,y) { a<-na.omit(x) b<-na.omit(y) return(dtw(a,b,distance.only=TRUE)$normalizedDistance) } ## create a new entry in the registry with two aliases pr_DB$set_entry(FUN = dtwOmitNA, names = c("dtwOmitNA")) d<-dist(dataset, method = "dtwOmitNA")`

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")
```

.

Istituto di Neuroscienze (ISIB-IN-CNR)

Consiglio Nazionale delle Ricerche

Padova, Italy

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

I am also interested in hearing from companies interested in using DTW for commercial purposes. The Istituto di Neuroscienze may provide on-site and/or remote consultancy.

$Id: index.php 439 2019-04-24 19:06:22Z tonig $