The R Journal: accepted article

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Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa PDF download
Moreno I. Coco, Dan Mønster, Giuseppe Leonardi, Rick Dale and Sebastian Wallot

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.

Received: 2020-04-30; online 2021-06-21
CRAN packages: tseriesChaos, nonlinearTseries, RHRV, crqa
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-062,
  author = {Moreno I. Coco and Dan Mønster and Giuseppe Leonardi and
          Rick Dale and Sebastian Wallot},
  title = {{Unidimensional and Multidimensional Methods for Recurrence
          Quantification Analysis with crqa}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-062},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-062/index.html}
}