%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2018/04.02.11.56
%2 sid.inpe.br/mtc-m21c/2018/04.02.11.56.27
%@doi 10.1142/S0218127418500086
%@issn 0218-1274
%@issn 1793-6551
%T Recurrence density enhanced complex networks for nonlinear time series analysis
%D 2018
%8 jan.
%9 journal article
%A Costa, Diego G. de B.,
%A Reis, Barbara Maximino da Fonseca,
%A Zou, Yong,
%A Quiles, Marcos G.,
%A Macau, Elbert Einstein Nehrer,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation East China Normal University
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@affiliation Universidade Federal de São Paulo (UNIFESP)
%@electronicmailaddress
%@electronicmailaddress barbara.reis@inpe.br
%@electronicmailaddress yzou@phy.ecnu.edu.cn
%B International Journal of Bifurcation and Chaos
%V 28
%N 1
%P e1850008
%K Recurrence plot, recurrence networks, nonlinear time series.
%X We introduce a new method, which is entitled Recurrence Density Enhanced Complex Network (RDE-CN), to properly analyze nonlinear time series. Our method first transforms a recurrence plot into a figure of a reduced number of points yet preserving the main and fundamental recurrence properties of the original plot. This resulting figure is then reinterpreted as a complex network, which is further characterized by network statistical measures. We illustrate the computational power of RDE-CN approach by time series by both the logistic map and experimental fluid flows, which show that our method distinguishes different dynamics sufficiently well as the traditional recurrence analysis. Therefore, the proposed methodology characterizes the recurrence matrix adequately, while using a reduced set of points from the original recurrence plots.
%@language en
%3 costa_recurrence.pdf