  In this work a robust clustering algorithm for stationary time series is
proposed. The algorithm is based on the use of estimated spectral densities,
which are considered as functional data, as the basic characteristic of
stationary time series for clustering purposes. A robust algorithm for
functional data is then applied to the set of spectral densities. Trimming
techniques and restrictions on the scatter within groups reduce the effect of
noise in the data and help to prevent the identification of spurious clusters.
The procedure is tested in a simulation study, and is also applied to a real
data set.
