Title
Distinguishing Noise From Chaos: Objective Versus Subjective Criteria Using Horizontal Visibility Graph
Abstract
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(kappa)similar to exp(-lambda kappa), in which kappa is the node degree and lambda 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.
Year
DOI
Venue
2014
10.1371/journal.pone.0108004
PLOS ONE
Keywords
Field
DocType
medicine,biology,engineering,chemistry,physics
Information theory,Applied mathematics,Visibility graph,Stochastic process,White noise,Dynamical systems theory,Probability distribution,Degree distribution,Chaotic,Physics
Journal
Volume
Issue
ISSN
9
9
1932-6203
Citations 
PageRank 
References 
5
0.71
4
Authors
5