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 ( \pm 15 ) .
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 ( \pm 2 ) – 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 .