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 …

Complex networks are an important paradigm of modern complex systems sciences which
allows quantitatively assessing the structural properties of systems composed of different …

Revealing complicated behaviors from time series constitutes a fundamental problem of
continuing interest and it has attracted a great deal of attention from a wide variety of fields …

This paper presents a new approach for analysing the structural properties of time series
from complex systems. Starting from the concept of recurrences in phase space, the …

Visibility graph has established itself as a powerful tool for analyzing time series. We in this
paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG) …

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 …

Nonlinear time series analysis is an active field of research that studies the structure of
complex signals in order to derive information of the process that generated those series, for …

Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant
importance. Combining adaptive optimal kernel time-frequency representation and visibility …

We propose a method to measure real-valued time series irreversibility which combines two
different tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This …

Recently, the visibility graph (VG) algorithm was proposed for map** a time series to a
graph to study complexity and fractality of the time series through investigation of the …