  The precise physical process that triggers solar flares is not currently
understood. Here we attempt to capture the signature of this mechanism in solar
image data of various wavelengths and use these signatures to predict flaring
activity. We do this by developing an algorithm that [1] automatically
generates features in 5.5 TB of image data taken by the Solar Dynamics
Observatory of the solar photosphere, chromosphere, transition region, and
corona during the time period between May 2010 and May 2014, [2] combines these
features with other features based on flaring history and a physical
understanding of putative flaring processes, and [3] classifies these features
to predict whether a solar active region will flare within a time period of $T$
hours, where $T$ = 2 and 24. We find that when optimizing for the True Skill
Score (TSS), photospheric vector magnetic field data combined with flaring
history yields the best performance, and when optimizing for the area under the
precision-recall curve, all the data are helpful. Our model performance yields
a TSS of $0.84 \pm 0.03$ and $0.81 \pm 0.03$ in the $T$ = 2 and 24 hour cases,
respectively, and a value of $0.13 \pm 0.07$ and $0.43 \pm 0.08$ for the area
under the precision-recall curve in the $T$ = 2 and 24 hour cases,
respectively. These relatively high scores are similar to, but not greater
than, other attempts to predict solar flares. Given the similar values of
algorithm performance across various types of models reported in the
literature, we conclude that we can expect a certain baseline predictive
capacity using these data. This is the first attempt to predict solar flares
using photospheric vector magnetic field data as well as multiple wavelengths
of image data from the chromosphere, transition region, and corona.
