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dc.contributor.authorLeles, M. C. R.en
dc.contributor.authorCardoso, A. S. V.en
dc.contributor.authorMoreira, M. G.en
dc.contributor.authorGuimaraes, H. N.en
dc.contributor.authorSilva, C. M.en
dc.contributor.authorPitsillides, Andreasen
dc.creatorLeles, M. C. R.en
dc.creatorCardoso, A. S. V.en
dc.creatorMoreira, M. G.en
dc.creatorGuimaraes, H. N.en
dc.creatorSilva, C. M.en
dc.creatorPitsillides, Andreasen
dc.description.abstractSingular Spectrum Analysis (SSA) is a nonparametric approach used to decompose a time series into meaningful components, related to trends, oscillations and noise. SSA can be seen as a spectral decomposition, where each term is related to an eigenvector derived from the trajectory matrix. In this context the eigenvectors can be viewed as eigenfilters. The frequency domain interpretation of SSA is a relatively recent subject. Although the analytic solution for the frequency-response of eigenfilters is already known, the periodogram is often applied for their frequency characterization. This paper presents a comparison of these methods, applied to eigenfilters' frequency characterization for time series components identification. To perform this evaluation, several tests were carried out, in both a synthetic and real data time series. In every situations the eigenfilters analytic frequency response method provided better results compared to the periodogram in terms of frequency estimates as well as their dispersion and sensitivity to variations in the SSA algorithm parameter. © 2016 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.source2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016en
dc.source2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016en
dc.subjectTime seriesen
dc.subjectSensitivity analysisen
dc.subjectFrequency responseen
dc.subjectSignal processingen
dc.subjectEigenvalues and eigenfunctionsen
dc.subjectFrequency domain analysisen
dc.subjectSpectrum analysisen
dc.subjectTime series analysisen
dc.subjectComponents identificationsen
dc.subjectFrequency characterizationen
dc.subjectFrequency response methodsen
dc.subjectNonparametric approachesen
dc.subjectSensitivity to variationsen
dc.subjectSingular spectrum analysisen
dc.subjectSpectral decompositionen
dc.subjectSynthetic and real dataen
dc.titleFrequency-domain characterization of Singular Spectrum Analysis eigenvectorsen
dc.description.endingpage27 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notesConference code: 126985</p>en
dc.contributor.orcidPitsillides, Andreas [0000-0001-5072-2851]

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