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

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 …

The capacity to identify the unique functional architecture of an individual's brain is a crucial
step toward personalized medicine and understanding the neural basis of variation in …

We present the first algorithms that allow the estimation of non-negative Lyapunov
exponents from an experimental time series. Lyapunov exponents, which provide a …

Statistical mechanics is used to describe the observed information processing complexity of
nonlinear dynamical systems. We introduce a measure of complexity distinct from and dual …

Quantities are defined operationally which qualify as measures of complexity of patterns
arising in physical situations. Their main features, distinguishing them from previously used …

Defining structure and detecting the emergence of complexity in nature are inherently
subjective, though essential, scientific activities. Despite the difficulties, these problems can …

Temporal pattern learning, control and prediction, and chaotic data analysis share a
common problem: deducing optimal equations of motion from observations of time …

This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis
to the multivariate case. This is achieved by introducing multivariate sample entropy …

Computational mechanics, an approach to structural complexity, defines a process's causal
states and gives a procedure for finding them. We show that the causal-state representation …