Robust clustering for time series using spectral densities and functional data analysis

Diego Rivera-García, Luis Angel García-Escudero, Agustín Mayo-Iscar, Joaquín Ortega

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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 appliedtoarealdataset.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag service@springer.de
ISBN (Print)9783319591469
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Fingerprint

Dive into the research topics of 'Robust clustering for time series using spectral densities and functional data analysis'. Together they form a unique fingerprint.

Cite this