Quantile graphs for the characterization of chaotic dynamics in time series

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dc.contributor.authorLopes de Oliveira Campanharo, Andriana Susana [UNESP]
dc.contributor.authorRamos, Fernando Manuel
dc.contributor.authorEssaaidi, M.
dc.contributor.authorNemiche, M.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionInst Nacl Pesquisas Espaciais
dc.date.accessioned2018-11-26T15:44:05Z
dc.date.available2018-11-26T15:44:05Z
dc.date.issued2015-01-01
dc.description.abstractRecently, a map from time series to networks with an approximate inverse operation has been proposed [1], allowing the use network statistics to characterize time series and time series statistics to characterize networks. In this approach, time series quantiles are mapped into nodes of a graph [1], [2]. Here we show these quantile graphs (QGs) are able to characterize features such as long range correlations or deterministic chaos present in the underlying dynamics of the original signal, making them a powerful tool for the analysis of nonlinear systems. As an illustration we applied the QG method to the Logistic and the Quadratic maps, for varying values of their control parameters. We show that in both cases the main features of resulting bifurcation cascades, with their progressive transition from periodic behavior to chaos, are well captured by the topology of QGs.en
dc.description.affiliationUniv Estadual Paulista, Inst Biociencias, Dept Bioestat, BR-18603560 Sao Paulo, Brazil
dc.description.affiliationInst Nacl Pesquisas Espaciais, Lab Comp & Matemat Aplicada, BR-30332025 Sao Paulo, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Inst Biociencias, Dept Bioestat, BR-18603560 Sao Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2014/05145-0
dc.description.sponsorshipIdFAPESP: 2013/19905-3
dc.format.extent4
dc.identifier.citationProceedings Of 2015 Third Ieee World Conference On Complex Systems (wccs). New York: Ieee, 4 p., 2015.
dc.identifier.urihttp://hdl.handle.net/11449/159506
dc.identifier.wosWOS:000399131300092
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartofProceedings Of 2015 Third Ieee World Conference On Complex Systems (wccs)
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectNonlinear Time Series
dc.subjectChaotic System
dc.subjectQuantile Graphs
dc.subjectComplex Networks
dc.titleQuantile graphs for the characterization of chaotic dynamics in time seriesen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee

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