  We present a new method for time series clustering which we call the
Hierarchical Spectral Merger (HSM) method. This procedure is based on the
spectral theory of time series and identifies series that share similar
oscillations or waveforms. The extent of similarity between a pair of time
series is measured using the total variation distance between their estimated
spectral densities. At each step of the algorithm, every time two clusters
merge, a new spectral density is estimated using the whole information present
in both clusters, which is representative of all the series in the new cluster.
The method is implemented in an R package HSMClust. We present two applications
of the HSM method, one to data coming from wave-height measurements in
oceanography and the other to electroencefalogram (EEG) data.
