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.

翻译：业务照明弹预测旨在提供可用于通常每天对耀斑发生的空间天气影响作出决定的预测,该研究显示,在生成用于优化网络的培训和验证成套材料同时计算太阳周期周期时,可以将基于视频的深层学习用于业务目的,具体而言,本文件描述了一种算法,可用于建立根据与特定周期阶段相关的耀斑等级率平衡的活跃区域组,这些组数用于培训和验证由脉冲神经网络和长短时间记忆网络相结合的长期连续革命网络,在2015年3月和2017年9月分别包含太阳风暴的两个预测窗口中评估了这一方法的可靠性。