Contrasting chaotic with stochastic dynamics via ordinal transition networks

Autores
Olivares, F.; Olivares, F.; Zanin, M.; Zanin, M.; Zunino, Luciano José; Zunino, Luciano José; Pérez, D.G.; Pérez, D.G.
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.
We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.
Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Zanin, M.. Universidad Politécnica de Madrid; España
Fil: Zanin, M.. Universidad Politécnica de Madrid; España
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina
Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile
Materia
CHAOS
CHAOS
NONLINEAR DYNAMICS
NONLINEAR DYNAMICS
STOCHASTIC PROCESSES
STOCHASTIC PROCESSES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/144097
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Contrasting chaotic with stochastic dynamics via ordinal transition networksContrasting chaotic with stochastic dynamics via ordinal transition networksOlivares, F.Olivares, F.Zanin, M.Zanin, M.Zunino, Luciano JoséZunino, Luciano JoséPérez, D.G.Pérez, D.G.CHAOSCHAOSNONLINEAR DYNAMICSNONLINEAR DYNAMICSSTOCHASTIC PROCESSESSTOCHASTIC PROCESSEShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; ChileFil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; ChileFil: Zanin, M.. Universidad Politécnica de Madrid; EspañaFil: Zanin, M.. Universidad Politécnica de Madrid; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; ArgentinaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; ArgentinaFil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; ChileFil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; ChileAmerican Institute of PhysicsAmerican Institute of Physics2020-06-012020-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/144097Olivares, F.; Zanin, M.; Zunino, Luciano José; Pérez, D.G.; Contrasting chaotic with stochastic dynamics via ordinal transition networks; American Institute of Physics; Chaos; 30; 6; 1-6-2020; 1-13Olivares, F.; Zanin, M.; Zunino, Luciano José; Pérez, D.G.; Contrasting chaotic with stochastic dynamics via ordinal transition networks; American Institute of Physics; Chaos; 30; 6; 1-6-2020; 1-131054-15001054-15001089-76821089-7682CONICET DigitalCONICETengenginfo:eu-repo/semantics/altIdentifier/url/http://aip.scitation.org/doi/10.1063/1.5142500info:eu-repo/semantics/altIdentifier/url/http://aip.scitation.org/doi/10.1063/1.5142500info:eu-repo/semantics/altIdentifier/doi/10.1063/1.5142500info:eu-repo/semantics/altIdentifier/doi/10.1063/1.5142500info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:04:00Zoai:ri.conicet.gov.ar:11336/144097instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:04:00.567CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Contrasting chaotic with stochastic dynamics via ordinal transition networks
Contrasting chaotic with stochastic dynamics via ordinal transition networks
title Contrasting chaotic with stochastic dynamics via ordinal transition networks
spellingShingle Contrasting chaotic with stochastic dynamics via ordinal transition networks
Olivares, F.
CHAOS
CHAOS
NONLINEAR DYNAMICS
NONLINEAR DYNAMICS
STOCHASTIC PROCESSES
STOCHASTIC PROCESSES
title_short Contrasting chaotic with stochastic dynamics via ordinal transition networks
title_full Contrasting chaotic with stochastic dynamics via ordinal transition networks
title_fullStr Contrasting chaotic with stochastic dynamics via ordinal transition networks
title_full_unstemmed Contrasting chaotic with stochastic dynamics via ordinal transition networks
title_sort Contrasting chaotic with stochastic dynamics via ordinal transition networks
dc.creator.none.fl_str_mv Olivares, F.
Olivares, F.
Zanin, M.
Zanin, M.
Zunino, Luciano José
Zunino, Luciano José
Pérez, D.G.
Pérez, D.G.
author Olivares, F.
author_facet Olivares, F.
Zanin, M.
Zunino, Luciano José
Pérez, D.G.
author_role author
author2 Zanin, M.
Zunino, Luciano José
Pérez, D.G.
author2_role author
author
author
dc.subject.none.fl_str_mv CHAOS
CHAOS
NONLINEAR DYNAMICS
NONLINEAR DYNAMICS
STOCHASTIC PROCESSES
STOCHASTIC PROCESSES
topic CHAOS
CHAOS
NONLINEAR DYNAMICS
NONLINEAR DYNAMICS
STOCHASTIC PROCESSES
STOCHASTIC PROCESSES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.
We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.
Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Olivares, F.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Zanin, M.. Universidad Politécnica de Madrid; España
Fil: Zanin, M.. Universidad Politécnica de Madrid; España
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina
Fil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina
Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile
Fil: Pérez, D.G.. Pontificia Universidad Católica de Valparaíso; Chile
description We introduce a representation space to contrast chaotic with stochastic dynamics. Following the complex network representation of a time series through ordinal pattern transitions, we propose to assign each system a position in a two-dimensional plane defined by the permutation entropy of the network (global network quantifier) and the minimum value of the permutation entropy of the nodes (local network quantifier). The numerical analysis of representative chaotic maps and stochastic systems shows that the proposed approach is able to distinguish linear from non-linear dynamical systems by different planar locations. Additionally, we show that this characterization is robust when observational noise is considered. Experimental applications allow us to validate the numerical findings and to conclude that this approach is useful in practical contexts.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-01
2020-06-01
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/11336/144097
Olivares, F.; Zanin, M.; Zunino, Luciano José; Pérez, D.G.; Contrasting chaotic with stochastic dynamics via ordinal transition networks; American Institute of Physics; Chaos; 30; 6; 1-6-2020; 1-13
Olivares, F.; Zanin, M.; Zunino, Luciano José; Pérez, D.G.; Contrasting chaotic with stochastic dynamics via ordinal transition networks; American Institute of Physics; Chaos; 30; 6; 1-6-2020; 1-13
1054-1500
1054-1500
1089-7682
1089-7682
CONICET Digital
CONICET
url http://hdl.handle.net/11336/144097
identifier_str_mv Olivares, F.; Zanin, M.; Zunino, Luciano José; Pérez, D.G.; Contrasting chaotic with stochastic dynamics via ordinal transition networks; American Institute of Physics; Chaos; 30; 6; 1-6-2020; 1-13
1054-1500
1089-7682
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://aip.scitation.org/doi/10.1063/1.5142500
info:eu-repo/semantics/altIdentifier/url/http://aip.scitation.org/doi/10.1063/1.5142500
info:eu-repo/semantics/altIdentifier/doi/10.1063/1.5142500
info:eu-repo/semantics/altIdentifier/doi/10.1063/1.5142500
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Institute of Physics
American Institute of Physics
publisher.none.fl_str_mv American Institute of Physics
American Institute of Physics
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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