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Time Series, Spectral Densities and Robust Functional Clustering

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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

Research by DRG and JO was partially supported by Conacyt, Mexico Project 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, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, Grants VA005P17 and VA002G18.

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Authors and Affiliations

  1. Centro de Investigación en Matemáticas, Guanajuato, Mexico

    D. Rivera-García & J. Ortega

  2. Universidad de Valladolid, Valladolid, Spain

    L. A. García-Escudero & A. Mayo-Iscar

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  1. D. Rivera-García
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  2. L. A. García-Escudero
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  3. A. Mayo-Iscar
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  4. J. Ortega
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Correspondence to J. Ortega.

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Rivera-García, D., García-Escudero, L.A., Mayo-Iscar, A. et al. Time Series, Spectral Densities and Robust Functional Clustering. Neural Process Lett 52, 135–152 (2020). https://doi.org/10.1007/s11063-018-9926-1

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