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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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lit-22-wal-04-four.pdf (Publisher version), 6MB
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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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 Creators:
Tomashin, Alon1, Author
Leonardi, Giuseppe2, Author
Wallot, Sebastian3, 4, Author                 
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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Free keywords: recurrence quantification analysis; fractals; monofractals; fractal time series
 Abstract: 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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Language(s): eng - English
 Dates: 2022-08-292022-07-192022-03-132022-09-19
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/e24091314
 Degree: -

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Title: Entropy
  Abbreviation : Entropy
Source Genre: Journal
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Publ. Info: Basel : MDPI
Pages: - Volume / Issue: 24 (9) Sequence Number: - Start / End Page: - Identifier: ISSN: 1099-4300
CoNE: https://pure.mpg.de/cone/journals/resource/110978984445793