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Journal Article

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

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Citation

Cocoa, M. I., Mønster, D., Leonardi, G., Rick, D., & Wallot, S. (2020). Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa. arXiv. Retrieved from https://arxiv.org/abs/2006.01954.


Cite as: http://hdl.handle.net/21.11116/0000-0006-A817-3
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 autorecurrence 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.