{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:21:58Z","timestamp":1759450918975,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"crossref","award":["PID2023-147827NB-I00"],"award-info":[{"award-number":["PID2023-147827NB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Entropy"],"abstract":"<jats:p>Classical spectral analysis characterizes linear systems effectively but often fails to reveal the nonlinear temporal structure of chaotic dynamics. We introduce the ordinal spectrum, a frequency-domain characterization derived from the ordinal-pattern representation of a time series. Applied to both synthetic and real-world datasets\u2014including periodic, stochastic, and chaotic signals from physical, biological, and astronomical sources\u2014the ordinal spectrum identifies the temporal scales implied in a possible chaotic behavior. By providing an interpretable, data-driven view of symbolic dynamics in the frequency domain, this approach complements state\u2013space reconstructions and enhances the detection of nonlinear temporal organization that classical spectra may obscure. Its ability to distinguish between qualitatively different dynamics make it a useful tool for exploring complex time series across diverse scientific domains.<\/jats:p>","DOI":"10.3390\/e27101027","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T15:38:55Z","timestamp":1759246735000},"page":"1027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ordinal Spectrum: Mapping Ordinal Patterns into Frequency Domain"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-4833","authenticated-orcid":false,"given":"Mario","family":"Chavez","sequence":"first","affiliation":[{"name":"CNRS UMR-7225, H\u00f4pital de la Salp\u00eatri\u00e8re, 75013 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3365-8189","authenticated-orcid":false,"given":"Johann H.","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Complex Systems Group and G.I.S.C, Universidad Rey Juan Carlos, 28933 M\u00f3stoles, Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1103\/RevModPhys.65.1331","article-title":"The analysis of observed chaotic data in physical systems","volume":"65","author":"Abarbanel","year":"1993","journal-title":"Rev. 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