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We study the prediction of solar flare size and time-to-flare using 38features describing magnetic complexity of the photospheric magnetic field.This work uses support vector regression to formulate a mapping from the38-dimensional feature space to a continuous-valued label vector representingflare size or time-to-flare. When we consider flaring regions only, we find anaverage error in estimating flare size of approximately half a\emph{geostationary operational environmental satellite} (\emph{GOES}) class.When we additionally consider non-flaring regions, we find an increased averageerror of approximately 3/4 a \emph{GOES} class. We also consider thresholdingthe regressed flare size for the experiment containing both flaring andnon-flaring regions and find a true positive rate of 0.69 and a true negativerate of 0.86 for flare prediction. The results for both of these sizeregression experiments are consistent across a wide range of predictive timewindows, indicating that the magnetic complexity features may be persistent inappearance long before flare activity. This is supported by our larger errorrates of some 40 hr in the time-to-flare regression problem. The 38 magneticcomplexity features considered here appear to have discriminative potential forflare size, but their persistence in time makes them less discriminative forthe time-to-flare problem.

Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression