A model-free, data-based forecast for sunspot cycle 25

TL;DR

This paper employs reservoir computing, a neural network technique, to forecast sunspot cycle 25, predicting a weak cycle lasting about ten years with a peak in 2024 and a maximum of around 113 sunspots.

Contribution

It introduces a model-free, data-driven forecasting method for solar cycles using reservoir computing, including a novel variation for improved amplitude prediction.

Findings

Cycle 25 will last about ten years.

Peak sunspot activity expected in 2024.

Cycle 25 likely weaker than average, similar to cycle 24.

Abstract

The dynamic activity of the Sun, governed by its cycle of sunspots -- strongly magnetized regions that are observed on its surface -- modulate our solar system space environment creating space weather. Severe space weather leads to disruptions in satellite operations, telecommunications, electric power grids and air-traffic on polar routes. Forecasting the cycle of sunspots, however, has remained a challenging problem. We use reservoir computing -- a model-free, neural--network based machine-learning technique -- to forecast the upcoming solar cycle, sunspot cycle 25. The standard algorithm forecasts that solar cycle 25 is going to last about ten years, the maxima is going to appear in the year 2024 and the maximum number of sunspots is going to be 113 ($\pm15$). We also develop a novel variation of the standard algorithm whose forecasts for duration and peak timing matches that of the standard algorithm, but whose peak amplitude forecast is 124 ($\pm2$) -- within the upper bound of the standard reservoir computing algorithm. We conclude that sunspot cycle 25 is likely to be a weak, lower than average solar cycle, somewhat similar in strength to sunspot cycle 24.