Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa
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 to 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 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.
Cite
@article{arxiv.2006.01954,
title = {Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa},
author = {Moreno I. Coco and Dan Mønster and Giuseppe Leonardi and Rick Dale and Sebastian Wallot},
journal= {arXiv preprint arXiv:2006.01954},
year = {2023}
}
Comments
Describes R package crqa v. 2.0: https://cran.r-project.org/web/packages/crqa/