@Article{CostaReiZouQuiMac:2018:ReDeEn,
author = "Costa, Diego G. de B. and Reis, Barbara Maximino da Fonseca and
Zou, Yong and Quiles, Marcos G. and Macau, Elbert Einstein
Nehrer",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {East China Normal
University} and {Universidade Federal de S{\~a}o Paulo (UNIFESP)}
and {Universidade Federal de S{\~a}o Paulo (UNIFESP)}",
title = "Recurrence density enhanced complex networks for nonlinear time
series analysis",
journal = "International Journal of Bifurcation and Chaos",
year = "2018",
volume = "28",
number = "1",
pages = "e1850008",
month = "jan.",
keywords = "Recurrence plot, recurrence networks, nonlinear time series.",
abstract = "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.",
doi = "10.1142/S0218127418500086",
url = "http://dx.doi.org/10.1142/S0218127418500086",
issn = "0218-1274 and 1793-6551",
language = "en",
targetfile = "costa_recurrence.pdf",
urlaccessdate = "2024, Apr. 28"
}