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 …

There is nowadays a constant flux of data being generated and collected in all types of real
world systems. These data sets are often indexed by time, space, or both requiring …

A growing number of algorithms have been proposed to map a scalar time series into
ordinal partition transition networks. However, most observable phenomena in the empirical …

Identifying structural damage is an essential task for ensuring the safety and functionality of
civil, mechanical, and aerospace structures. In this study, the structural damage identification …

Uncertainty plays an important role in the global economy. In this paper, the economic policy
uncertainty (EPU) indices of the United States and China are selected as the proxy variable …

We propose a cross horizontal visibility graph (CHVG) algorithm to explore associations
between two time series. As a natural extension of the classic horizontal visibility graph …

We investigate whether it is possible to distinguish chaotic time series from random time
series using network theory. In this perspective, we selected four methods to generate …

In this paper, the Sun and its behavior are studied by means of complex networks. The
complex network was built using the Visibility Graph algorithm. This method maps time …

We propose the construction of cross and joint ordinal pattern transition networks from
multivariate time series for two coupled systems, where synchronizations are often present …

Recently time series prediction based on network analysis has become a hot research topic.
However, how to more accurately forecast time series with good efficiency is still an open …