Detecting dynamical changes in time series by using the Jensen Shannon divergence

Autores
Mateos, Diego Martín; Riveaud, Leonardo Esteban; Lamberti, Pedro Walter
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.
http://aip.scitation.org/doi/10.1063/1.4999613
info:eu-repo/semantics/publishedVersion
Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Otras Ciencias Físicas
Materia
Chaos
Noise
Jensen Shannon divergence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/553789
Acceder
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oai_identifier_str oai:rdu.unc.edu.ar:11086/553789
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Detecting dynamical changes in time series by using the Jensen Shannon divergenceMateos, Diego MartínRiveaud, Leonardo EstebanLamberti, Pedro WalterChaosNoiseJensen Shannon divergenceFil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.http://aip.scitation.org/doi/10.1063/1.4999613info:eu-repo/semantics/publishedVersionFil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Otras Ciencias Físicashttps://orcid.org/0000-0002-1953-08752017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/octet-streamMateos, D. M., Riveaud, L. E. y Lamberti, P. W. (2017). Detecting dynamical changes in time series by using the Jensen Shannon divergence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27 (8), 083118. https://dx.doi.org/10.1063/1.49996131054-1500http://hdl.handle.net/11086/5537891089-7682https://dx.doi.org/10.1063/1.4999613enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-11T10:19:35Zoai:rdu.unc.edu.ar:11086/553789Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-11 10:19:35.229Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Detecting dynamical changes in time series by using the Jensen Shannon divergence
title Detecting dynamical changes in time series by using the Jensen Shannon divergence
spellingShingle Detecting dynamical changes in time series by using the Jensen Shannon divergence
Mateos, Diego Martín
Chaos
Noise
Jensen Shannon divergence
title_short Detecting dynamical changes in time series by using the Jensen Shannon divergence
title_full Detecting dynamical changes in time series by using the Jensen Shannon divergence
title_fullStr Detecting dynamical changes in time series by using the Jensen Shannon divergence
title_full_unstemmed Detecting dynamical changes in time series by using the Jensen Shannon divergence
title_sort Detecting dynamical changes in time series by using the Jensen Shannon divergence
dc.creator.none.fl_str_mv Mateos, Diego Martín
Riveaud, Leonardo Esteban
Lamberti, Pedro Walter
author Mateos, Diego Martín
author_facet Mateos, Diego Martín
Riveaud, Leonardo Esteban
Lamberti, Pedro Walter
author_role author
author2 Riveaud, Leonardo Esteban
Lamberti, Pedro Walter
author2_role author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0002-1953-0875
dc.subject.none.fl_str_mv Chaos
Noise
Jensen Shannon divergence
topic Chaos
Noise
Jensen Shannon divergence
dc.description.none.fl_txt_mv Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.
http://aip.scitation.org/doi/10.1063/1.4999613
info:eu-repo/semantics/publishedVersion
Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Otras Ciencias Físicas
description Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
status_str publishedVersion
format article
dc.identifier.none.fl_str_mv Mateos, D. M., Riveaud, L. E. y Lamberti, P. W. (2017). Detecting dynamical changes in time series by using the Jensen Shannon divergence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27 (8), 083118. https://dx.doi.org/10.1063/1.4999613
1054-1500
http://hdl.handle.net/11086/553789
1089-7682
https://dx.doi.org/10.1063/1.4999613
identifier_str_mv Mateos, D. M., Riveaud, L. E. y Lamberti, P. W. (2017). Detecting dynamical changes in time series by using the Jensen Shannon divergence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27 (8), 083118. https://dx.doi.org/10.1063/1.4999613
1054-1500
1089-7682
url http://hdl.handle.net/11086/553789
https://dx.doi.org/10.1063/1.4999613
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/octet-stream
dc.source.none.fl_str_mv reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 12.993085

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