Signal separation with almost periodic components: A wavelets based method

Autores
Rosso, O.A.; Figliola, A.; Blanco, S.; Jacovkis, P.M.
Año de publicación
2004
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Natural time series usually show either a combination of periodic phenomena with stochastic components or chaotic behavior. In many cases, when nonlinear characteristics are computed, they will essentially indicate the most remarkable effects and the results will underestimate or overestimate the real complexity of the system. For that reason signal separation of the frequency bands representing well known phenomena, like periodic or almost periodic behaviors, allows comprehension of the hidden nonlinear or stochastic phenomena involved. In this work a signal separation method based on trigonometric wavelet packets is described. The method has been applied, as an example, to a time series of daily mean discharges of the Atuel river in Argentina, that presents strong annual and semiannual oscillations due to meteorological effects. The correlation dimension and the maximum Lyapunov exponent of the residual time series were obtained taking away its known almost periodic components.
Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Blanco, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Jacovkis, P.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
Rev. Mex. Fis. 2004;50(2):179-186
Materia
Meteorological time series
Signal separation
Time-frequency signal analysis
Wavelet analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_0035001X_v50_n2_p179_Rosso
Acceder
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oai_identifier_str paperaa:paper_0035001X_v50_n2_p179_Rosso
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Signal separation with almost periodic components: A wavelets based methodRosso, O.A.Figliola, A.Blanco, S.Jacovkis, P.M.Meteorological time seriesSignal separationTime-frequency signal analysisWavelet analysisNatural time series usually show either a combination of periodic phenomena with stochastic components or chaotic behavior. In many cases, when nonlinear characteristics are computed, they will essentially indicate the most remarkable effects and the results will underestimate or overestimate the real complexity of the system. For that reason signal separation of the frequency bands representing well known phenomena, like periodic or almost periodic behaviors, allows comprehension of the hidden nonlinear or stochastic phenomena involved. In this work a signal separation method based on trigonometric wavelet packets is described. The method has been applied, as an example, to a time series of daily mean discharges of the Atuel river in Argentina, that presents strong annual and semiannual oscillations due to meteorological effects. The correlation dimension and the maximum Lyapunov exponent of the residual time series were obtained taking away its known almost periodic components.Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Blanco, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Jacovkis, P.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2004info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_0035001X_v50_n2_p179_RossoRev. Mex. Fis. 2004;50(2):179-186reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-10-23T11:18:19Zpaperaa:paper_0035001X_v50_n2_p179_RossoInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-10-23 11:18:20.167Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Signal separation with almost periodic components: A wavelets based method
title Signal separation with almost periodic components: A wavelets based method
spellingShingle Signal separation with almost periodic components: A wavelets based method
Rosso, O.A.
Meteorological time series
Signal separation
Time-frequency signal analysis
Wavelet analysis
title_short Signal separation with almost periodic components: A wavelets based method
title_full Signal separation with almost periodic components: A wavelets based method
title_fullStr Signal separation with almost periodic components: A wavelets based method
title_full_unstemmed Signal separation with almost periodic components: A wavelets based method
title_sort Signal separation with almost periodic components: A wavelets based method
dc.creator.none.fl_str_mv Rosso, O.A.
Figliola, A.
Blanco, S.
Jacovkis, P.M.
author Rosso, O.A.
author_facet Rosso, O.A.
Figliola, A.
Blanco, S.
Jacovkis, P.M.
author_role author
author2 Figliola, A.
Blanco, S.
Jacovkis, P.M.
author2_role author
author
author
dc.subject.none.fl_str_mv Meteorological time series
Signal separation
Time-frequency signal analysis
Wavelet analysis
topic Meteorological time series
Signal separation
Time-frequency signal analysis
Wavelet analysis
dc.description.none.fl_txt_mv Natural time series usually show either a combination of periodic phenomena with stochastic components or chaotic behavior. In many cases, when nonlinear characteristics are computed, they will essentially indicate the most remarkable effects and the results will underestimate or overestimate the real complexity of the system. For that reason signal separation of the frequency bands representing well known phenomena, like periodic or almost periodic behaviors, allows comprehension of the hidden nonlinear or stochastic phenomena involved. In this work a signal separation method based on trigonometric wavelet packets is described. The method has been applied, as an example, to a time series of daily mean discharges of the Atuel river in Argentina, that presents strong annual and semiannual oscillations due to meteorological effects. The correlation dimension and the maximum Lyapunov exponent of the residual time series were obtained taking away its known almost periodic components.
Fil:Figliola, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Blanco, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Jacovkis, P.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description Natural time series usually show either a combination of periodic phenomena with stochastic components or chaotic behavior. In many cases, when nonlinear characteristics are computed, they will essentially indicate the most remarkable effects and the results will underestimate or overestimate the real complexity of the system. For that reason signal separation of the frequency bands representing well known phenomena, like periodic or almost periodic behaviors, allows comprehension of the hidden nonlinear or stochastic phenomena involved. In this work a signal separation method based on trigonometric wavelet packets is described. The method has been applied, as an example, to a time series of daily mean discharges of the Atuel river in Argentina, that presents strong annual and semiannual oscillations due to meteorological effects. The correlation dimension and the maximum Lyapunov exponent of the residual time series were obtained taking away its known almost periodic components.
publishDate 2004
dc.date.none.fl_str_mv 2004
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_0035001X_v50_n2_p179_Rosso
url http://hdl.handle.net/20.500.12110/paper_0035001X_v50_n2_p179_Rosso
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Rev. Mex. Fis. 2004;50(2):179-186
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 12.982451

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