Change-Point Detection Using the Conditional Entropy of Ordinal Patterns

Anton M Unakafov; Karsten Keller

doi:10.3390/e20090709

Change-Point Detection Using the Conditional Entropy of Ordinal Patterns

Entropy (Basel). 2018 Sep 14;20(9):709. doi: 10.3390/e20090709.

Authors

Anton M Unakafov^{1

2

3

4

5}, Karsten Keller¹

Affiliations

¹ Institute of Mathematics, University of Lübeck, 23562 Lübeck, Germany.
² Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, 23562 Lübeck, Germany.
³ Georg-Elias-Müller-Institute of Psychology, University of Goettingen, Goßlerstraße 14, 37073 Goettingen, Germany.
⁴ Theoretical Neurophysics Group, Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Goettingen, Germany.
⁵ Leibniz ScienceCampus Primate Cognition, Kellnerweg 4, 37077 Goettingen, Germany.

Abstract

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.

Keywords: change-point detection; conditional entropy; ordinal pattern.

Grants and funding

DFG GSC 235/1/Deutsche Forschungsgemeinschaft