rqa

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====== Recurrence Quantification Analysis ======

Recurrence (Quantification) Analysis (RQA) is a nonlinear data analysis technique used for studying dynamical systems. It can be applied to any time series coming from a dynamical system that repeats itself (conservative system) – most of the real-world complex phenomena fall into this category. Recurrence plot is a visual representation of a dynamical process. RQA measures quantify meaningful properties of the system (kind of organization, predictability, stability, etc.).

Recurrence analysis is intuitively simple: if we conceptualize time as 1D line, we can make a 2D pattern representing “meetings” between points in time, a “meeting” occurs when the system is in the same state for both time points. It is also well-grounded in dynamical systems theory (starting point: [[https://en.wikipedia.org/wiki/Poincar%C3%A9_recurrence_theorem|Poincaré recurrence theorem]]).

**I want to learn RQA, what do I do?**

  - Visit the page: [[http://www.recurrence-plot.tk]]. It is the main source of information about the technique and has nice introductory materials.
  - If you want to delve into theory, you may read [[https://www.sciencedirect.com/science/article/abs/pii/S0370157306004066|Marwan, M. C. Romano, M. Thiel, J. Kurths: Recurrence Plots for the Analysis of Complex Systems, Physics Reports, 438(5-6), 237-329.]]
  - Without delving into theory, you may go through some introductory slides. Here are [[http://cognaction.org/rdmaterials/ati/|Rick Dale's materials]].
  - Find a software package that suits you, go through some tutorials and play with your own data.

[[https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02232/full|Cross-recurrence Quantification Analysis in R tutorial]]

Very good, clean and efficient implementation of RQA techniques is a part of [[https://juliadynamics.github.io/DynamicalSystems.jl/stable/|DynamicalSystems.jl]] package for Julia (highly recommended).

A comprehensive implementation of most standard recurrence analysis techniques and supplementary heuristics can be found in R in [[https://cran.r-project.org/web/packages/crqa/index.html|cRQA package]]. (Not so efficient for larger volumes of data.)

**Demos**

RQA plots online: [[https://hill.psych.uw.edu.pl/rqa_js/]]

Live RQA demo on human motion (webcam required): [[https://hill.psych.uw.edu.pl/frame_diff_rqa/]]

**Who do I ask for help?**

  * [[j.zubek@uw.edu.pl|Julian Zubek]]