Attention-based machine vision models and techniques for solar wind
speed forecasting using solar EUV images
Extreme ultraviolet images taken by the Atmospheric Imaging Assembly on
board the Solar Dynamics Observatory make it possible to use deep vision
techniques to forecast solar wind speed - a difficult, high-impact, and
unsolved problem. At a four day time horizon, this study uses
attention-based models and a set of methodological improvements to
deliver an 11.1% lower RMSE error and a 17.4% higher prediction
correlation compared to the previous work testing on the period from
2010 to 2018. Our analysis shows that attention-based models combined
with our pipeline consistently outperform convolutional alternatives.
Our model has learned relationships between coronal holes’
characteristics and the speed of their associated high speed streams,
agreeing with empirical results. Our study finds a strong dependence of
our best model on the position in the solar cycle, with the best
performance occurring in the declining phase.