Time series characterization via horizontal visibility graph and Information Theory

Autores
Gonçalves, Bruna Amin; Carpi, Laura; Rosso, Osvaldo Aníbal; Ravetti, Martín G.
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degreedistribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.
Fil: Gonçalves, Bruna Amin. Universidade Federal de Minas Gerais; Brasil
Fil: Carpi, Laura. Universidad Politécnica de Catalunya; España
Fil: Rosso, Osvaldo Aníbal. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ravetti, Martín G.. Universidade Federal de Minas Gerais; Brasil
Materia
Time Series Analysis
Complex Networks
Information Theory Quantifiers
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/46546
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Time series characterization via horizontal visibility graph and Information TheoryGonçalves, Bruna AminCarpi, LauraRosso, Osvaldo AníbalRavetti, Martín G.Time Series AnalysisComplex NetworksInformation Theory Quantifiershttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degreedistribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.Fil: Gonçalves, Bruna Amin. Universidade Federal de Minas Gerais; BrasilFil: Carpi, Laura. Universidad Politécnica de Catalunya; EspañaFil: Rosso, Osvaldo Aníbal. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ravetti, Martín G.. Universidade Federal de Minas Gerais; BrasilElsevier Science2016-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/46546Gonçalves, Bruna Amin; Carpi, Laura; Rosso, Osvaldo Aníbal; Ravetti, Martín G.; Time series characterization via horizontal visibility graph and Information Theory; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 464; 12-2016; 93-1020378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.07.063info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437116304940info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:24Zoai:ri.conicet.gov.ar:11336/46546instacron: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 09:40:25.262CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Time series characterization via horizontal visibility graph and Information Theory
title Time series characterization via horizontal visibility graph and Information Theory
spellingShingle Time series characterization via horizontal visibility graph and Information Theory
Gonçalves, Bruna Amin
Time Series Analysis
Complex Networks
Information Theory Quantifiers
title_short Time series characterization via horizontal visibility graph and Information Theory
title_full Time series characterization via horizontal visibility graph and Information Theory
title_fullStr Time series characterization via horizontal visibility graph and Information Theory
title_full_unstemmed Time series characterization via horizontal visibility graph and Information Theory
title_sort Time series characterization via horizontal visibility graph and Information Theory
dc.creator.none.fl_str_mv Gonçalves, Bruna Amin
Carpi, Laura
Rosso, Osvaldo Aníbal
Ravetti, Martín G.
author Gonçalves, Bruna Amin
author_facet Gonçalves, Bruna Amin
Carpi, Laura
Rosso, Osvaldo Aníbal
Ravetti, Martín G.
author_role author
author2 Carpi, Laura
Rosso, Osvaldo Aníbal
Ravetti, Martín G.
author2_role author
author
author
dc.subject.none.fl_str_mv Time Series Analysis
Complex Networks
Information Theory Quantifiers
topic Time Series Analysis
Complex Networks
Information Theory Quantifiers
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degreedistribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.
Fil: Gonçalves, Bruna Amin. Universidade Federal de Minas Gerais; Brasil
Fil: Carpi, Laura. Universidad Politécnica de Catalunya; España
Fil: Rosso, Osvaldo Aníbal. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ravetti, Martín G.. Universidade Federal de Minas Gerais; Brasil
description Complex networks theory have gained wider applicability since methods for transformation of time series to networks were proposed and successfully tested. In the last few years, horizontal visibility graph has become a popular method due to its simplicity and good results when applied to natural and artificially generated data. In this work, we explore different ways of extracting information from the network constructed from the horizontal visibility graph and evaluated by Information Theory quantifiers. Most works use the degreedistribution of the network, however, we found alternative probability distributions, more efficient than the degree distribution in characterizing dynamical systems. In particular, we find that, when using distributions based on distances and amplitude values, significant shorter time series are required. We analyze fractional Brownian motion time series, and a paleoclimatic proxy record of ENSO from the Pallcacocha Lake to study dynamical changes during the Holocene.
publishDate 2016
dc.date.none.fl_str_mv 2016-12
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/11336/46546
Gonçalves, Bruna Amin; Carpi, Laura; Rosso, Osvaldo Aníbal; Ravetti, Martín G.; Time series characterization via horizontal visibility graph and Information Theory; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 464; 12-2016; 93-102
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/46546
identifier_str_mv Gonçalves, Bruna Amin; Carpi, Laura; Rosso, Osvaldo Aníbal; Ravetti, Martín G.; Time series characterization via horizontal visibility graph and Information Theory; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 464; 12-2016; 93-102
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2016.07.063
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437116304940
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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 13.070432

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