  Improving the performance of solar flare forecasting is a hot topic in solar
physics research field. Deep learning has been considered a promising approach
to perform solar flare forecasting in recent years. We first used the
Generative Adversarial Networks (GAN) technique augmenting sample data to
balance samples with different flare classes. We then proposed a hybrid
convolutional neural network (CNN) model M for forecasting flare eruption in a
solar cycle. Based on this model, we further investigated the effects of the
rising and declining phases for flare forecasting. Two CNN models, i.e., Mrp
and Mdp, were presented to forecast solar flare eruptions in the rising phase
and declining phase of solar cycle 24, respectively. A series of testing
results proved: 1) Sample balance is critical for the stability of the CNN
model. The augmented data generated by GAN effectively improved the stability
of the forecast model. 2) For C-class, M-class, and X-class flare forecasting
using Solar Dynamics Observatory (SDO) line-of-sight (LOS) magnetograms, the
means of true skill statistics (TSS) score of M are 0.646, 0.653 and 0.762,
which improved by 20.1%, 22.3%, 38.0% compared with previous studies. 3) It is
valuable to separately model the flare forecasts in the rising and declining
phases of a solar cycle. Compared with model M, the means of TSS score for
No-flare, C-class, M-class, X-class flare forecasting of the Mrp improved by
5.9%, 9.4%, 17.9% and 13.1%, and the Mdp improved by 1.5%, 2.6%, 11.5% and
12.2%.
