Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Border effect corrections for diagonal line based recurrence quantification analysis measures

Urheber*innen
/persons/resource/hkraemer

Krämer,  Kai-Hauke
Potsdam Institute for Climate Impact Research;

/persons/resource/Marwan

Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)

23376oa.pdf
(Postprint), 7MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Krämer, K.-H., Marwan, N. (2019): Border effect corrections for diagonal line based recurrence quantification analysis measures. - Physics Letters A, 383, 34, 125977.
https://doi.org/10.1016/j.physleta.2019.125977


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_23376
Zusammenfassung
Recurrence Quantification Analysis (RQA) defines a number of quantifiers, which base upon diagonal line structures in the recurrence plot (RP). Due to the finite size of an RP, these lines can be cut by the borders of the RP and, thus, bias the length distribution of diagonal lines and, consequently, the line based RQA measures. In this letter we investigate the impact of the mentioned border effects and of the thickening of diagonal lines in an RP (caused by tangential motion) on the estimation of the diagonal line length distribution, quantified by its entropy. Although a relation to the Lyapunov spectrum is theoretically expected, the mentioned entropy yields contradictory results in many studies. Here we summarize correction schemes for both, the border effects and the tangential motion and systematically compare them to methods from the literature. We show that these corrections lead to the expected behavior of the diagonal line length entropy, in particular meaning zero values in case of a regular motion and positive values for chaotic motion. Moreover, we test these methods under noisy conditions, in order to supply practical tools for applied statistical research.