Change-point detection using the conditional entropy of ordinal patterns

TL;DR

This paper introduces a change-point detection method based on the conditional entropy of ordinal patterns, which captures local dynamics in time series with minimal prior information and demonstrates strong numerical performance.

Contribution

The paper presents a novel change-point detection approach using conditional entropy of ordinal patterns, requiring minimal assumptions and showing effective results.

Findings

Effective detection of change-points in time series.

Requires minimal prior information.

Shows good performance in numerical experiments.

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.