Compute the dissimilarity matrix between a set of single-variate timeseries.

`dtwDist(mx, my = mx, ...)`

mx

numeric matrix, containing timeseries as rows

my

numeric matrix, containing timeseries as rows (for cross-distance)

...

arguments passed to the `dtw()`

call

A square matrix whose element `[i,j]`

holds the Dynamic Time
Warp distance between row `i`

(query) and `j`

(reference) of
`mx`

and `my`

, i.e. `dtw(mx[i,],my[j,])$distance`

.

`dtwDist`

computes a dissimilarity matrix, akin to `dist()`

,
based on the Dynamic Time Warping definition of a distance between
single-variate timeseries.

The `dtwDist`

command is a synonym for the `proxy::dist()`

function of package proxy; the DTW distance is registered as
`method="DTW"`

(see examples below).

The timeseries are stored as rows in the matrix argument `m`

. In other
words, if `m`

is an N * T matrix, `dtwDist`

will build N*N ordered
pairs of timeseries, perform the corresponding N*N `dtw`

alignments,
and return all of the results in a matrix. Each of the timeseries is T
elements long.

`dtwDist`

returns a square matrix, whereas the `dist`

object is
lower-triangular. This makes sense because in general the DTW "distance" is
not symmetric (see e.g. asymmetric step patterns). To make a square matrix
with the `proxy::dist()`

function sematics, use the two-arguments
call as `dist(m,m)`

. This will return a square `crossdist`

object.

# NOT RUN { ## Symmetric step pattern => symmetric dissimilarity matrix; ## no problem coercing it to a dist object: m <- matrix(0,ncol=3,nrow=4) m <- row(m) dist(m,method="DTW"); # Old-fashioned call style would be: # dtwDist(m) # as.dist(dtwDist(m)) ## Find the optimal warping _and_ scale factor at the same time. ## (There may be a better, analytic way) # Prepare a query and a reference query<-sin(seq(0,4*pi,len=100)) reference<-cos(seq(0,4*pi,len=100)) # Make a set of several references, scaled from 0 to 3 in .1 increments. # Put them in a matrix, in rows scaleSet <- seq(0.1,3,by=.1) referenceSet<-outer(1/scaleSet,reference) # The query has to be made into a 1-row matrix. # Perform all of the alignments at once, and normalize the result. dist(t(query),referenceSet,meth="DTW")->distanceSet # The optimal scale for the reference is 1.0 plot(scaleSet,scaleSet*distanceSet, xlab="Reference scale factor (denominator)", ylab="DTW distance",type="o", main="Sine vs scaled cosine alignment, 0 to 4 pi") ## Asymmetric step pattern: we can either disregard part of the pairs ## (as.dist), or average with the transpose mm <- matrix(runif(12),ncol=3) dm <- dist(mm,mm,method="DTW",step=asymmetric); # a crossdist object # Old-fashioned call style would be: # dm <- dtwDist(mm,step=asymmetric) # as.dist(dm) ## Symmetrize by averaging: (dm+t(dm))/2 ## check definition stopifnot(dm[2,1]==dtw(mm[2,],mm[1,],step=asymmetric)$distance) # }