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Open AccessArticle

Change-Point Detection Using the Conditional Entropy of Ordinal Patterns

by Anton M. Unakafov 1,2,3,4,5 and Karsten Keller 1,*
Institute of Mathematics, University of Lübeck, 23562 Lübeck, Germany
Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, 23562 Lübeck, Germany
Georg-Elias-Müller-Institute of Psychology, University of Goettingen, Goßlerstraße 14, 37073 Goettingen, Germany
Theoretical Neurophysics Group, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Goettingen, Germany
Leibniz ScienceCampus Primate Cognition, Kellnerweg 4, 37077 Goettingen, Germany
Author to whom correspondence should be addressed.
Entropy 2018, 20(9), 709;
Received: 27 July 2018 / Revised: 3 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)
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. View Full-Text
Keywords: change-point detection; conditional entropy; ordinal pattern change-point detection; conditional entropy; ordinal pattern
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Unakafov, A.M.; Keller, K. Change-Point Detection Using the Conditional Entropy of Ordinal Patterns. Entropy 2018, 20, 709.

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