Abstract
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.
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Acknowledgements
Data for station 160 were furnished by the Coastal Data Information Program (CDIP), Integrative Oceanographic Division, operated by the Scripps Institution of Oceanography (http://cdip.ucsd.edu/). Research by DRG and JO was partially supported by Conacyt, Mexico Proyecto 169175 Análisis Estadístico de Olas Marinas, Fase II. Research by LA G-E and A M-I was partially supported by the Spanish Ministerio de Economía y Competitividad y fondos FEDER, grant MTM2014-56235-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León, grant VA212U13.
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Rivera-García, D., García-Escudero, L.A., Mayo-Iscar, A., Ortega, J. (2017). Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_13
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