Time Series, Spectral Densities and Robust Functional Clustering

D. Rivera-García, L. A. García-Escudero, A. Mayo-Iscar, J. Ortega

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
JournalNeural Processing Letters
StatePublished - Jan 1 2018
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2019-11-20


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