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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-062.zip
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For attribution, please cite this work as
Coco, et al., "Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa", The R Journal, 2021
BibTeX citation
@article{RJ-2021-062,
author = {Coco, Moreno I. and Mønster, Dan and Leonardi, Giuseppe and Dale, Rick and Wallot, Sebastian},
title = {Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-062},
doi = {10.32614/RJ-2021-062},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {112-130}
}