`vignettes/Multivariate.Rmd`

`Multivariate.Rmd`

This is a minimal working example that demonstrates how multivariate
time series can be clustered with the `pdc`

package. Note
that `pdc`

can only capture limited information in
multivariate time series and there are likely other approaches that are
better suited to deal with multivariate time series.

First, we load the package and simulate six two-dimensional time series, each with 5000 observations. The first pair are simulated from an autoregressive process with AR=0.1, the second pair are simulated with AR=0.4.

Note that to this end, data must be arranged in a three-dimensional
array, with the first dimension representing time, the second dimension
identity of the time series, and the third dimension the dimension of
the time series. Here, we create data according to
`dim=c(5000,6,2)`

that is 6 unique time series with each two
channels and 5,000 observed time points.

```
library(pdc)
set.seed(902101)
ar_params <- rep(c(0.1,0.4), each=3)
X <- array(data = NA, dim=c(5000,6,2))
for (i in 1:dim(X)[2]) {
for (j in 1:dim(X)[3]) {
X[,i,j] <- arima.sim(list(ar=ar_params[i]),n=dim(X)[1])
}
}
labels <- paste("2D-ARIMA ",ar_params,sep="")
```

Run clustering and plot result