Transfer entropy rate through Lempel-Ziv complexity

Autores
Restrepo Rinckoar, Juan Felipe; Mateos, Diego Martín; Schlotthauer, Gaston
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.
Fil: Restrepo Rinckoar, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Mateos, Diego Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Autónoma de Entre Ríos. Facultad de Ciencia y Tecnología; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Materia
Lempel-Ziv complexity
Transfer Entropy
Chaos
Non Linear Dynamic
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/142909
Acceder
id CONICETDig_11810c0e3b2aa138f7d55986a3f6650f
oai_identifier_str oai:ri.conicet.gov.ar:11336/142909
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Transfer entropy rate through Lempel-Ziv complexityRestrepo Rinckoar, Juan FelipeMateos, Diego MartínSchlotthauer, GastonLempel-Ziv complexityTransfer EntropyChaosNon Linear Dynamichttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.Fil: Restrepo Rinckoar, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Mateos, Diego Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Autónoma de Entre Ríos. Facultad de Ciencia y Tecnología; ArgentinaFil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaAmerican Physical Society2020-05-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/142909Restrepo Rinckoar, Juan Felipe; Mateos, Diego Martín; Schlotthauer, Gaston; Transfer entropy rate through Lempel-Ziv complexity; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 101; 5; 15-5-2020; 1-92470-0053CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.aps.org/doi/10.1103/PhysRevE.101.052117info:eu-repo/semantics/altIdentifier/doi//10.1103/PhysRevE.101.052117info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1903.07720info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:57:58Zoai:ri.conicet.gov.ar:11336/142909instacron: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:34982025-09-29 09:57:58.738CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Transfer entropy rate through Lempel-Ziv complexity
title Transfer entropy rate through Lempel-Ziv complexity
spellingShingle Transfer entropy rate through Lempel-Ziv complexity
Restrepo Rinckoar, Juan Felipe
Lempel-Ziv complexity
Transfer Entropy
Chaos
Non Linear Dynamic
title_short Transfer entropy rate through Lempel-Ziv complexity
title_full Transfer entropy rate through Lempel-Ziv complexity
title_fullStr Transfer entropy rate through Lempel-Ziv complexity
title_full_unstemmed Transfer entropy rate through Lempel-Ziv complexity
title_sort Transfer entropy rate through Lempel-Ziv complexity
dc.creator.none.fl_str_mv Restrepo Rinckoar, Juan Felipe
Mateos, Diego Martín
Schlotthauer, Gaston
author Restrepo Rinckoar, Juan Felipe
author_facet Restrepo Rinckoar, Juan Felipe
Mateos, Diego Martín
Schlotthauer, Gaston
author_role author
author2 Mateos, Diego Martín
Schlotthauer, Gaston
author2_role author
author
dc.subject.none.fl_str_mv Lempel-Ziv complexity
Transfer Entropy
Chaos
Non Linear Dynamic
topic Lempel-Ziv complexity
Transfer Entropy
Chaos
Non Linear Dynamic
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.
Fil: Restrepo Rinckoar, Juan Felipe. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
Fil: Mateos, Diego Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Autónoma de Entre Ríos. Facultad de Ciencia y Tecnología; Argentina
Fil: Schlotthauer, Gaston. Consejo Nacional de Investigaciones Científicas y Técnicas. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina
description The transfer entropy and the transfer entropy rate are closely related concepts that measure information exchange between two dynamical systems. These measures allow us to study linear and nonlinear causality relations and can be estimated through the use of different methodologies. However, some of them assume a data model and/or are computationally expensive. This article depicts a methodology to estimate the transfer entropy rate between two systems through the Lempel-Ziv complexity. This methodology offers a set of advantages: It estimates the transfer entropy rate from two single discrete series of measures, it is not computationally expensive, and it does not assume any data model. The simulation results over three different unidirectional coupled dynamical systems suggest that this methodology can be used to assess the direction and strength of the information flow between systems. Moreover, it provides good estimations for short-length time series.
publishDate 2020
dc.date.none.fl_str_mv 2020-05-15
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/11336/142909
Restrepo Rinckoar, Juan Felipe; Mateos, Diego Martín; Schlotthauer, Gaston; Transfer entropy rate through Lempel-Ziv complexity; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 101; 5; 15-5-2020; 1-9
2470-0053
CONICET Digital
CONICET
url http://hdl.handle.net/11336/142909
identifier_str_mv Restrepo Rinckoar, Juan Felipe; Mateos, Diego Martín; Schlotthauer, Gaston; Transfer entropy rate through Lempel-Ziv complexity; American Physical Society; Physical Review E: Statistical, Nonlinear and Soft Matter Physics; 101; 5; 15-5-2020; 1-9
2470-0053
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.aps.org/doi/10.1103/PhysRevE.101.052117
info:eu-repo/semantics/altIdentifier/doi//10.1103/PhysRevE.101.052117
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1903.07720
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
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
_version_ 1844613731223339008
score 13.070432

Publicaciones similares