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.
