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
- Institución
- Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
- OAI Identificador
- paperaa:paper_0035001X_v50_n2_p179_Rosso
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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/ar2025-09-29T13:42:55Zpaperaa: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:18962025-09-29 13:42:56.378Biblioteca 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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1844618735463170048 |
score |
13.070432 |