Quantile graphs for the characterization of chaotic dynamics in time series

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dc.contributor.authorDe Oliveira Campanharo, Andriana Susana Lopes [UNESP]
dc.contributor.authorRamos, Fernando Manuel [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-29T07:50:08Z
dc.date.available2022-04-29T07:50:08Z
dc.date.issued2016-06-01
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.affiliationDepartamento de Bioestatística Instituto de Biociências Universidade Estadual Paulista
dc.description.affiliationUnespDepartamento de Bioestatística Instituto de Biociências Universidade Estadual Paulista
dc.identifierhttp://dx.doi.org/10.1109/ICoCS.2015.7483302
dc.identifier.citationProceedings of 2015 IEEE World Conference on Complex Systems, WCCS 2015.
dc.identifier.doi10.1109/ICoCS.2015.7483302
dc.identifier.scopus2-s2.0-84978437340
dc.identifier.urihttp://hdl.handle.net/11449/228184
dc.language.isoeng
dc.relation.ispartofProceedings of 2015 IEEE World Conference on Complex Systems, WCCS 2015
dc.sourceScopus
dc.subjectChaotic System
dc.subjectComplex Networks
dc.subjectNonlinear Time Series
dc.subjectQuantile Graphs
dc.titleQuantile graphs for the characterization of chaotic dynamics in time seriesen
dc.typeTrabalho apresentado em evento

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Artigos - Bioestatística - IBB