Abstract
Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package includes implementations of several recent advances in recurrence based analysis, among them applications to multivariate data and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.
Highlights
In the current article, we present the updated 2.0 version of the R package crqa to perform many variants of recurrence-based analyses (Coco and Dale, 2014), including some very recent developments for the treatment of multivariate and categorical data
In the new version of the crqa package, we provide the user with the piecewiseRQA function, which can be used to compute all different variants of recurrence quantification analysis described above on long time series
This paper describes recurrence quantification analysis, a statistical method to characterize the nonlinear dynamics of a system
Summary
We present the updated 2.0 version of the R package crqa to perform many variants of recurrence-based analyses (Coco and Dale, 2014), including some very recent developments for the treatment of multivariate and categorical data. The real success of recurrence-based analyses has revolved around their power of capturing the dynamics of complex and non-stationary time series data and of time series exhibiting qualitatively different patterns along with their temporal evolution (Marwan et al, 2007). This is because recurrencebased analyses are model-free techniques that make few assumptions and are well suited for the analysis of complex systems. Recurrence-based analyses are versatile and can be applied to interval-scale data as well as nominal data, continuously sampled data, and inter-event data alike (Dale et al, 2011; Zbilut et al, 1998)
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