Department of Economics and Business Economics

Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa

Research output: Working paperResearch


  • Moreno Coco, University of East London, Universidade de Lisboa, United Kingdom
  • Dan Mønster
  • Giuseppe Leonardi, University of Economics and Human Sciences in Warsaw, Poland
  • Rick Dale, University of California, United States
  • Sebastian Wallot, Max Planck Institute for Empirical Aesthetics, Germany
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.
Original languageEnglish
Number of pages30
Publication statusPublished - 28 May 2020

    Research areas

  • recurrence quantification analysis, unidimensional and multidimensional time series, non-linear dynamics, R-package

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