man-examples/dba.R

In asardaes/dtwclust: Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

# Sample data
data(uciCT)

# Obtain an average for the first 5 time series
dtw_avg <- DBA(CharTraj[1:5], CharTraj[[1]], trace = TRUE)

# Plot
matplot(do.call(cbind, CharTraj[1:5]), type = "l")
points(dtw_avg)

# Change the provided order
dtw_avg2 <- DBA(CharTraj[5:1], CharTraj[[1]], trace = TRUE)

# Same result?
all.equal(dtw_avg, dtw_avg2)

\dontrun{
# ====================================================================================
# Multivariate versions
# ====================================================================================

# sample centroid reference
cent <- CharTrajMV[[3L]]
# sample series
x <- CharTrajMV[[1L]]
# sample set of series
X <- CharTrajMV[1L:5L]

# the by-series version does something like this for each series and the centroid
alignment <- dtw_basic(x, cent, backtrack = TRUE)
# alignment$index1 and alginment$index2 indicate how to map x to cent (row-wise)

# the by-variable version treats each variable separately
alignment1 <- dtw_basic(x[,1L], cent[,1L], backtrack = TRUE)
alignment2 <- dtw_basic(x[,2L], cent[,2L], backtrack = TRUE)
alignment3 <- dtw_basic(x[,3L], cent[,3L], backtrack = TRUE)

# effectively doing:
X1 <- lapply(X, function(x) { x[,1L] })
X2 <- lapply(X, function(x) { x[,2L] })
X3 <- lapply(X, function(x) { x[,3L] })

dba1 <- dba(X1, cent[,1L])
dba2 <- dba(X2, cent[,2L])
dba3 <- dba(X3, cent[,3L])

new_cent <- cbind(dba1, dba2, dba3)

# sanity check
newer_cent <- dba(X, cent, mv.ver = "by-variable")
all.equal(newer_cent, new_cent, check.attributes = FALSE) # ignore names

}