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

本研究证明了，考虑太阳周期性的情况下，基于视频的深度学习可以用于操作性耀斑预测，并描述了一个用于构建平衡活动区集的算法来训练和验证包含卷积神经网络和长短期记忆网络的长期循环卷积网络，该方法以 2015 年 3 月和 2017 年 9 月的太阳风暴为例来评估其效果。