Nonlinear Correlations in Multifractals: Visibility Graphs of Magnitude and Sign Series

TL;DR

This paper introduces a novel method using visibility graphs to detect linear and nonlinear correlations in multifractal time series, overcoming limitations of traditional scaling-based approaches.

Contribution

It presents a distribution-independent topological parameter derived from visibility graphs to measure nonlinear correlations without requiring scaling region identification.

Findings

Detects nonlinear correlations in multifractal series effectively

Introduces a topological parameter independent of probability distribution

Works even when traditional methods consider series as uncorrelated

Abstract

Correlations in multifractal series have been investigated, extensively. Almost all approaches try to find scaling features of a given time series. However, the analysis of such scaling properties has some difficulties such as finding a proper scaling region. On the other hand, such correlation detection methods may be affected by the probability distribution function of the series. In this article, we apply the horizontal visibility graph algorithm to map stochastic time series into networks. By investigating the magnitude and sign of a multifractal time series, we show that one can detect linear as well as nonlinear correlations, even for situations that have been considered as uncorrelated noises by typical approaches like MFDFA. In this respect, we introduce a topological parameter that can well measure the strength of nonlinear correlations. This parameter is independent of the probability distribution function and calculated without the need to find any scaling region. Our findings may provide new insights about the multifractal analysis of time series in a variety of complex systems.