Change-Point Detection Using the Conditional Entropy of Ordinal Patterns
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Institute of Mathematics, University of Lübeck, 23562 Lübeck, Germany
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Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, 23562 Lübeck, Germany
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Georg-Elias-Müller-Institute of Psychology, University of Goettingen, Goßlerstraße 14, 37073 Goettingen, Germany
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Theoretical Neurophysics Group, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Goettingen, Germany
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Leibniz ScienceCampus Primate Cognition, Kellnerweg 4, 37077 Goettingen, Germany
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(9), 709; https://doi.org/10.3390/e20090709
Received: 27 July 2018
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Revised: 3 September 2018
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Accepted: 12 September 2018
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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.
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Keywords:
change-point detection; conditional entropy; ordinal pattern
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MDPI and ACS Style
Unakafov, A.M.; Keller, K. Change-Point Detection Using the Conditional Entropy of Ordinal Patterns. Entropy 2018, 20, 709. https://doi.org/10.3390/e20090709
AMA Style
Unakafov AM, Keller K. Change-Point Detection Using the Conditional Entropy of Ordinal Patterns. Entropy. 2018; 20(9):709. https://doi.org/10.3390/e20090709
Chicago/Turabian StyleUnakafov, Anton M., and Karsten Keller. 2018. "Change-Point Detection Using the Conditional Entropy of Ordinal Patterns" Entropy 20, no. 9: 709. https://doi.org/10.3390/e20090709
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