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Entree


Quantile graphs for the characterization of chaotic dynamics in time series

Autor(es):
Lopes de Oliveira Campanharo, Andriana Susana ; Ramos, Fernando Manuel ; Essaaidi, M ; Nemiche, M
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: PROCEEDINGS OF 2015 THIRD IEEE WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS); v. N/A, p. 4-pg., 2015-01-01.
Resumo

Recently, 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. (AU)

Processo FAPESP: 14/05145-0 - International School and Conference on Network Science (NetSci 2014)
Beneficiário:Andriana Susana Lopes de Oliveira Campanharo
Modalidade de apoio: Auxílio à Pesquisa - Reunião - Exterior
Processo FAPESP: 13/19905-3 - Caracterização e análise de séries temporais fisiológicas e de redes complexas biológicas
Beneficiário:Andriana Susana Lopes de Oliveira Campanharo
Modalidade de apoio: Auxílio à Pesquisa - Regular