We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field .
This work uses support vector regression to formulate a mapping from the 38-dimensional feature space to a continuous-valued label vector representing flare size or time-to-flare .
When we consider flaring regions only , we find an average error in estimating flare size of approximately half a geostationary operational environmental satellite ( GOES ) class .
When we additionally consider non-flaring regions , we find an increased average error of approximately 3/4 a GOES class .
We also consider thresholding the regressed flare size for the experiment containing both flaring and non-flaring regions and find a true positive rate of 0.69 and a true negative rate of 0.86 for flare prediction .
The results for both of these size regression experiments are consistent across a wide range of predictive time windows , indicating that the magnetic complexity features may be persistent in appearance long before flare activity .
This is supported by our larger error rates of some 40 hr in the time-to-flare regression problem .
The 38 magnetic complexity features considered here appear to have discriminative potential for flare size , but their persistence in time makes them less discriminative for the time-to-flare problem .