Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis
This work addresses clustering challenges in time series analysis for domains like signal processing or finance, but it appears incremental as it combines existing techniques (spectral densities and functional data analysis) with robustness modifications.
The authors tackled the problem of clustering stationary time series by proposing a robust algorithm based on spectral densities treated as functional data, which was tested in simulations and applied to real data.
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.