{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:22:46Z","timestamp":1774495366705,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,14]],"date-time":"2018-09-14T00:00:00Z","timestamp":1536883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["DFG GSC 235\/1"],"award-info":[{"award-number":["DFG GSC 235\/1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper is devoted to change-point detection using only the ordinal structure of a time series. A statistic based on the conditional entropy of ordinal patterns characterizing the local up and down in a time series is introduced and investigated. The statistic requires only minimal a priori information on given data and shows good performance in numerical experiments. By the nature of ordinal patterns, the proposed method does not detect pure level changes but changes in the intrinsic pattern structure of a time series and so it could be interesting in combination with other methods.<\/jats:p>","DOI":"10.3390\/e20090709","type":"journal-article","created":{"date-parts":[[2018,9,14]],"date-time":"2018-09-14T10:57:59Z","timestamp":1536922679000},"page":"709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Change-Point Detection Using the Conditional Entropy of Ordinal Patterns"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6782-5099","authenticated-orcid":false,"given":"Anton M.","family":"Unakafov","sequence":"first","affiliation":[{"name":"Institute of Mathematics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"},{"name":"Graduate School for Computing in Medicine and Life Sciences, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"},{"name":"Georg-Elias-M\u00fcller-Institute of Psychology, University of Goettingen, Go\u00dflerstra\u00dfe 14, 37073 Goettingen, Germany"},{"name":"Theoretical Neurophysics Group, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Goettingen, Germany"},{"name":"Leibniz ScienceCampus Primate Cognition, Kellnerweg 4, 37077 Goettingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karsten","family":"Keller","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,14]]},"reference":[{"key":"ref_1","unstructured":"Basseville, M., and Nikiforov, I.V. 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