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 …

This review covers the group of data-analysis techniques collectively referred to as
symbolization or symbolic time-series analysis. Symbolization involves transformation of raw …

Chaos: from simple models to complex systems aims to guide science and engineering
students through chaos and nonlinear dynamics from classical examples to the most recent …

In 438 alphabetically-arranged essays, this work provides a useful overview of the core
mathematical background for nonlinear science, as well as its applications to key problems …

Downward causation is the controversial idea that 'higher'levels of organization can causally
influence behaviour at 'lower'levels of organization. Here I propose that we can gain traction …

In this chapter, I review the main methods and techniques of complex systems science. As a
first step, I distinguish among the broad patterns which recur across complex systems, the …

Inference of causality is central in nonlinear time series analysis and science in general. A
popular approach to infer causality between two processes is to measure the information …

There has been considerable debate in the literature about the relative merits of information
processing versus dynamical approaches to understanding cognitive processes. In this …

The main motivation of this paper is to introduce the permutation Jensen-Shannon distance,
a symbolic tool able to quantify the degree of similarity between two arbitrary time series …

Measurable dynamics has traditionally referred to ergodic theory, which is in some sense a
sister topic to dynamical systems and chaos theory. However, the topic has until recently …