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  Four methods to distinguish between fractal dimensions in time series through recurrence quantification analysis

Tomashin, A., Leonardi, G., & Wallot, S. (2022). Four methods to distinguish between fractal dimensions in time series through recurrence quantification analysis. Entropy, 24(9). doi:10.3390/e24091314.

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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

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 Urheber:
Tomashin, Alon1, Autor
Leonardi, Giuseppe2, Autor
Wallot, Sebastian3, 4, Autor                 
Affiliations:
1The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 5290002, Israel, ou_persistent22              
2Institute of Psychology, University of Economics and Human Sciences, 01-043 Warsaw, Poland, ou_persistent22              
3Department of Language and Literature, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421695              
4 Institute for Sustainability Education and Psychology, Leuphana University of Lüneburg, 21335 Lüneburg, Germany, ou_persistent22              

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Schlagwörter: recurrence quantification analysis; fractals; monofractals; fractal time series
 Zusammenfassung: Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.

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Sprache(n): eng - English
 Datum: 2022-08-292022-07-192022-03-132022-09-19
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.3390/e24091314
 Art des Abschluß: -

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Titel: Entropy
  Kurztitel : Entropy
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Basel : MDPI
Seiten: - Band / Heft: 24 (9) Artikelnummer: - Start- / Endseite: - Identifikator: ISSN: 1099-4300
CoNE: https://pure.mpg.de/cone/journals/resource/110978984445793