Using conditional entropy to identify periodicity

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 for just identifying periodic behaviour. In particular, it is robust against common aliasing issues found with other period-finding algorithms