Complex Network Approach to the Statistical Features of the Sunspot Series

TL;DR

This study applies complex network analysis to sunspot time series, revealing non-Gaussian degree distributions, long-term correlations, and potential predictability in solar minima using visibility graphs.

Contribution

It introduces a novel application of visibility-graph analysis to sunspot data, highlighting long-term correlations and differences between maxima and minima.

Findings

Degree distribution for maxima is non-Gaussian.

Degree distribution for minima is bimodal.

Long-term correlations are characterized by a power-law waiting time regime.

Abstract

Complex network approaches have been recently developed as an alternative framework to study the statistical features of time-series data. We perform a visibility-graph analysis on both the daily and monthly sunspot series. Based on the data, we propose two ways to construct the network: one is from the original observable measurements and the other is from a negative-inverse-transformed series. The degree distribution of the derived networks for the strong maxima has clear non-Gaussian properties, while the degree distribution for minima is bimodal. The long-term variation of the cycles is reflected by hubs in the network which span relatively large time intervals. Based on standard network structural measures, we propose to characterize the long-term correlations by waiting times between two subsequent events. The persistence range of the solar cycles has been identified over 15\,--\,1000 days by a power-law regime with scaling exponent $\gamma = 2.04$ of the occurrence time of the two subsequent and successive strong minima. In contrast, a persistent trend is not present in the maximal numbers, although maxima do have significant deviations from an exponential form. Our results suggest some new insights for evaluating existing models. The power-law regime suggested by the waiting times does indicate that there are some level of predictable patterns in the minima.