DTW is a dynamic programming algorithm that tries to find the optimum warping path between two series. Over the years, several variations have appeared in order to make the procedure faster or more efficient. Please refer to the included references for more information, especially Giorgino (2009), which is a good practical introduction.
Most optimizations require equal dimensionality, which means time series should have equal length. DTW itself does not require this, but it is relatively expensive to compute. Other distance definitions may be used, or series could be reinterpolated to a matching length (Ratanamahatana and Keogh 2004).
Other packages that are particularly leveraged here are the proxy package for distance matrix calculations and the dtw package for some of the core DTW calculations.
The main clustering function and entry point for this package is tsclust.
Please note the random number generator is set to L'Ecuyer-CMRG when dtwclust is attached
in an attempt to preserve reproducibility. You are free to change this afterwards if you wish.
See RNGkind.
For more information, please read the included package vignette, which can be accessed by typing
vignette("dtwclust").
tsclust, dist, dtw