Operational solar flare forecasting via video-based deep learning

Operational flare forecasting aims at providing predictions that can be used to make decisions, typically on a daily scale, about the space weather impacts of occurrence. This study shows video-based deep learning for operational purposes when training and validation sets network optimization are generated while accounting periodicity solar cycle. Specifically, this article describes an algorithm applied build up active regions balanced according class rates associated specific cycle phase. These train validate long-term recurrent convolutional made combination neural long short-term memory network. The reliability approach is assessed in case two prediction windows containing storms March 2015, June September 2017.