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dc.contributor.authorSenra Díaz, Eva 
dc.contributor.authorBógalo, Juan
dc.contributor.authorPoncela Blanco, María Del Pilar
dc.date.accessioned2021-01-27T12:44:16Z
dc.date.available2021-01-27T12:44:16Z
dc.date.issued2021
dc.identifier.bibliographicCitationSignal Processing, 2021, v. 179, n. 107824en
dc.identifier.issn0165-1684
dc.identifier.urihttp://hdl.handle.net/10017/45972
dc.description.abstractSometimes, it is of interest to single out the fluctuations associated to a given frequency. We propose a new variant of SSA, Circulant SSA (CiSSA), that allows to extract the signal associated to any frequency specified beforehand. This is a novelty when compared with other SSA procedures that need to iden- tify ex-post the frequencies associated to the extracted signals. We prove that CiSSA is asymptotically equivalent to these alternative procedures although with the advantage of avoiding the need of the subse- quent frequency identification. We check its good performance and compare it to alternative SSA methods through several simulations for linear and nonlinear time series. We also prove its validity in the nonsta- tionary case. We apply CiSSA in two different fields to show how it works with real data and find that it behaves successfully in both applications. Finally, we compare the performance of CiSSA with other state of the art techniques used for nonlinear and nonstationary signals with amplitude and frequency varying in time.en
dc.description.sponsorshipMINECO/FEDERes
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCirculant matricesen
dc.subjectPrincipal componentsen
dc.subjectSignal extractionen
dc.subjectSingular spectrum analysisen
dc.subjectSingular value decompositionen
dc.subjectAM-FM Signalsen
dc.titleCirculant singular spectrum analysis: a new automated procedure for signal extractionen
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaEconomíaes_ES
dc.subject.ecienciaEconomicsen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Economíaes_ES
dc.date.updated2021-01-27T10:55:51Z
dc.relation.publisherversionwww.elsevier.com/locate/sigproen
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1016/j.sigpro.2020.107824
dc.relation.projectIDnfo:eu-repo/grantAgreement/MINECO//ECO2015-70331-C2-1-R/ES/ANALISIS DE ACTIVIDAD ECONOMICA MEDIANTE INDICADORES Y EVALUACION DE POLITICAS PUBLICAS/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//ECO2015-66593-P/ES/\"BIG DATA\" Y DATOS COMPLEJOS EN EMPRESA Y FINANZAS/es_ES
dc.relation.projectIDECO2016-76818-C3-3-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107161GB-C32/ES/INVESTIGACIONES EN TECNOLOGIA Y SOSTENIBILIDAD, INNOVACION Y EL MEDIOAMBIENTE/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108079GB-C22/ES/NUEVOS METODOS ECONOMETRICOS PARA PREDICCION MACROECONOMICA Y ANALISIS DE POLITICAS ECONOMICAS/es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiAR/0000034760
dc.identifier.publicationtitleSignal Processingen
dc.identifier.publicationvolume179
dc.identifier.publicationissue107824


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