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

TL;DR

This study employs support vector regression with 38 magnetic complexity features to predict solar flare size and timing, achieving moderate accuracy and highlighting the features' potential and limitations for flare forecasting.

Contribution

It introduces a novel application of support vector regression to predict both flare size and time-to-flare using magnetic complexity features.

Findings

Average error of half a GOES class in flare size prediction for flaring regions.

Increased error when including non-flaring regions, about three-quarters of a GOES class.

True positive rate of 0.69 and true negative rate of 0.86 in flare prediction.

Abstract

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 \emph{geostationary operational environmental satellite} (\emph{GOES}) class. When we additionally consider non-flaring regions, we find an increased average error of approximately 3/4 a \emph{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.