In this talk a robust clustering algorithm for stationary time series is presented. 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