Ordinal patterns-based methodologies for distinguishing chaos from noise in discrete time series

Abstract

Here, Zanin and Olivares review the permutation patterns-based metrics used to distinguish chaos from stochasticity in discrete time series. They analyse their performance and computational cost, and compare their applicability to real-world time series.