  We consider the flare prediction problem that distinguishes flare-imminent
active regions that produce an M- or X-class flare in the future 24 hours, from
quiet active regions that do not produce any flare within $\pm 24$ hours. Using
line-of-sight magnetograms and parameters of active regions in two data
products covering Solar Cycle 23 and 24, we train and evaluate two deep
learning algorithms -- CNN and LSTM -- and their stacking ensembles. The
decisions of CNN are explained using visual attribution methods. We have the
following three main findings. (1) LSTM trained on data from two solar cycles
achieves significantly higher True Skill Scores (TSS) than that trained on data
from a single solar cycle with a confidence level of at least 0.95. (2) On data
from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM
and CNN using the TSS criterion achieves significantly higher TSS than the
"select-best" strategy with a confidence level of at least 0.95. (3) A visual
attribution method called Integrated Gradients is able to attribute the CNN's
predictions of flares to the emerging magnetic flux in the active region. It
also reveals a limitation of CNN as a flare prediction method using
line-of-sight magnetograms: it treats the polarity artifact of line-of-sight
magnetograms as positive evidence of flares.
