Using conditional entropy to identify periodicity

TL;DR

This paper introduces a novel period detection method using conditional entropy, demonstrating superior robustness and accuracy on real data compared to existing information-based techniques.

Contribution

The paper presents a new efficient and accurate period finding method based on conditional entropy, improving robustness against aliasing in real data.

Findings

Comparable performance to existing methods on simulated data

Superior accuracy and robustness on real data

Effective in identifying periodic behavior despite aliasing

Abstract

This paper presents a new period finding method based on conditional entropy that is both efficient and accurate. We demonstrate its applicability on simulated and real data. We find that it has comparable performance to other information-based techniques with simulated data but is superior with real data, both for finding periods and just identifying periodic behaviour. In particular, it is robust against common aliasing issues found with other period-finding algorithms.