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 …

In 2002, in a seminal article, Bandt and Pompe proposed a new methodology for the
analysis of complex time series, now known as Ordinal Analysis. The ordinal methodology is …

Abstract Machine learning methods are becoming increasingly important for the
development of materials science. In spite of this, the use of image analysis in the …

We introduce a topological approach for quantifying the dynamical complexity of time series.
A novel complex network of visibility graph family is proposed based on defining visibility …

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 …

Discretizing a nonlinear time series enables us to calculate its statistics fast and rigorously.
Before the turn of the century, the approach using partitions was dominant. In the last two …

The ordinal pattern-based complexity–entropy plane is a popular tool in nonlinear dynamics
for distinguishing stochastic signals (noise) from deterministic chaos. Its performance …

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

The main motivation of this paper is to introduce the ordinal diversity, a symbolic tool able to
quantify the degree of diversity of multiple time series. Analytical, numerical, and …

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 …