{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:12:00Z","timestamp":1773655920695,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Science Foundation","doi-asserted-by":"publisher","award":["442405852"],"award-info":[{"award-number":["442405852"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e24091314","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T21:47:27Z","timestamp":1663624047000},"page":"1314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis"],"prefix":"10.3390","volume":"24","author":[{"given":"Alon","family":"Tomashin","sequence":"first","affiliation":[{"name":"The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan 5290002, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-4851","authenticated-orcid":false,"given":"Giuseppe","family":"Leonardi","sequence":"additional","affiliation":[{"name":"Institute of Psychology, University of Economics and Human Sciences, 01-043 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3626-3940","authenticated-orcid":false,"given":"Sebastian","family":"Wallot","sequence":"additional","affiliation":[{"name":"Institute for Sustainability Education and Psychology, Leuphana University of L\u00fcneburg, 21335 L\u00fcneburg, Germany"},{"name":"Department of Language and Literature, Max Planck Institute of Empirical Aesthetics, 60322 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1126\/science.7892611","article-title":"1\/f noise in human cognition","volume":"267","author":"Gilden","year":"1995","journal-title":"Science"},{"key":"ref_2","first-page":"479","article-title":"The fractal dynamics of self-esteem and physical self","volume":"8","author":"Fortes","year":"2004","journal-title":"Nonlinear Dyn. 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