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Averaged recurrence quantification analysis
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DOI：10.1140/epjs/s11734-022-00686-4.png)
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Abstract
En 中文
AbstractRecurrence quantification analysis (RQA) is a well established method of nonlinear data analysis. In this work, we present a new strategy for an almost parameter-free RQA. The approach finally omits the choice of the threshold parameter by calculating the RQA measures for a range of thresholds (in fact recurrence rates). Specifically, we test the ability of the RQA measure determinism, to sort data with respect to their signal to noise ratios. We consider a periodic signal, simple chaotic logistic equation, and Lorenz system in the tested data set with different and even very small signal-to-noise ratios of lengths $$10^2, 10^3, 10^4,$$
10
2
,
10
3
,
10
4
,
and $$10^5$$
10
5
. To make the calculations possible, a new effective algorithm was developed for streamlining of the numerical operations on graphics processing unit (GPU).
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