Artículos de revistas
Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph
Fecha
2014-09Registro en:
Gómez Ravetti, Martín; Carpi, Laura C.; Gonçalves, Bruna Amin; Frery, Alejandro César; Rosso, Osvaldo Aníbal; Distinguishing Noise from Chaos: Objective versus Subjective Criteria Using Horizontal Visibility Graph; Public Library of Science; Plos One; 9; 9; 9-2014; 1-37; e108004
1932-6203
CONICET Digital
CONICET
Autor
Gómez Ravetti, Martín
Carpi, Laura C.
Gonçalves, Bruna Amin
Frery, Alejandro César
Rosso, Osvaldo Aníbal
Resumen
A recently proposed methodology called the Horizontal Visibility Graph (HVG) [Luque et al., Phys. Rev. E., 80, 046103 (2009)] that constitutes a geometrical simplification of the well known Visibility Graph algorithm [Lacasa et al., Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to study the distinction between deterministic and stochastic components in time series [L. Lacasa and R. Toral, Phys. Rev. E., 82, 036120 (2010)]. Specifically, the authors propose that the node degree distribution of these processes follows an exponential functional of the form P(κ)~exp(–λk), in which κ is the node degree and λ is a positive parameter able to distinguish between deterministic (chaotic) and stochastic (uncorrelated and correlated) dynamics. In this work, we investigate the characteristics of the node degree distributions constructed by using HVG, for time series corresponding to 28 chaotic maps, 2 chaotic flows and 3 different stochastic processes. We thoroughly study the methodology proposed by Lacasa and Toral finding several cases for which their hypothesis is not valid. We propose a methodology that uses the HVG together with Information Theory quantifiers. An extensive and careful analysis of the node degree distributions obtained by applying HVG allow us to conclude that the Fisher-Shannon information plane is a remarkable tool able to graphically represent the different nature, deterministic or stochastic, of the systems under study.