UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux

Abstract

We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6,160 different combinations of SDO/HMI data as input data, and simulated the prediction of GOES X-ray flux for 2 years (2011-2012) with one-hour cadence. We have found that direct comparison of the true skill statistics (TSS) from small cross-validation sets is ill-posed, and used the standard scores ($z$) of the TSS to compare the performance of the various prediction strategies. The $z$ of a strategy is a stochastic variable of the stochastically-chosen cross-validation dataset, and the $z$ for the three strategies best at predicting X, $\geq$M and $\geq$C class flares are better than the average $z$ of the 6,160 strategies by 2.3$\sigma$, 2.1$\sigma$, 3.8$\sigma$ confidence levels, respectively. The best three TSS values were $0.75\pm0.07$, $0.48\pm0.02$, and $0.56\pm0.04$, respectively.