Stability of cycle in samplogram and spurious cycles in solar activity

TL;DR

This paper introduces a method to distinguish genuine stable cycles from spurious ones in solar activity data by analyzing their stability through a samplogram, addressing limitations of traditional spectral analysis.

Contribution

The paper presents a novel approach using samplograms and stability analysis to identify true cycles in non-stationary signals like solar activity.

Findings

Spurious cycles can appear significant due to non-stationarity.

Stable cycles show consistent peaks under averaging and differencing.

Samplogram effectively visualizes cycle stability.

Abstract

The spectral analysis with stochastic significance cannot distinguish the spurious cycles effectively, because a non-stationary signal can make a significant peak in spectrum. I show that the random separation between the grand extremes such as grand maxima and minima in solar activity can make a spurious but significant peak in spectrum. And it is possible to pick even the weak stable cycle by applying an averaging down-sampling and the differencing to a random signal. This is because both operations variously change the height of peak in spectrum and the spurious cycle is in turn unstable in both operations. I introduce a samplogram showing these operations and stability intuitively.