Estimating Unknown Cycles in Geophysical data

TL;DR

This paper reviews and compares methods for estimating unknown or varying periods in geophysical time series, demonstrating their effectiveness through simulations and real data applications.

Contribution

It introduces a comprehensive comparison of existing methods for period estimation in geophysical data, including simulations and real-world applications.

Findings

Certain methods outperform others in accuracy and robustness

Performance varies with data characteristics and cycle variability

Generalizations of methods extend their applicability

Abstract

Examples of cyclic (periodic) behavior in geophysical data abound. In many cases the primary period is known, such as in daily measurements of rain, temperature, and sea level. However, many time series of measurements contain cycles of unknown or varying length. We consider the problem of estimating the unknown period in a time series.We review the basic methods, compare their performance through a simulation studyusing observed sea level data, apply them to an astronomical data set, and discuss generalizations of the methods.