In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

In the last two decades, there has been an explosion of interest in modeling the brain as a
network, where nodes correspond variously to brain regions or neurons, and edges …

Since Bandt and Pompe's seminal work, permutation entropy has been used in several
applications and is now an essential tool for time series analysis. Beyond becoming a …

Characterizing signal dynamics with network approaches have attracted significant attention
in nonlinear time series analysis. Among these approaches, ordinal networks have received …

Power-law distributions are widely used in computational and statistical investigations of
extreme events and complex systems. The usual technique to generate power-law …

We introduce a new method of characterizing the seismic complex systems using a
procedure of transformation from complex networks into time series. The undirected complex …

We extend Elsinger's work on chi-squared tests for independence using ordinal patterns and
investigate the general class of m-dependent ordinal patterns processes, to which belong …

There exist rich patterns in nonlinear dynamical processes, but they merge into averages in
traditional statistics-based time series analysis. Herein the multi-scale transition matrix is …

It is rarely possible to precisely characterise the system underlying a series of observations.
Hypothesis testing, which involves assessing simple assumptions about driving …

The goal of any nonlinear dynamical analysis of a data series is to extract features of the
dynamics of the underlying physical and chemical processes that produce that spatial …