UFCORIN: A fully automated predictor of solar flares in GOES X-ray flux

We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6160 different combinations of Solar Dynamic Observatory/Helioseismic and Magnetic Imager data as input data, and simulated the prediction of GOES X-ray flux for 2 years (2011-2012) with 1 h cadence. We have found that direct comparison of the true skill statistic (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 data set, and the z for the three strategies best at predicting X-, >= M-, and >= C-class flares are better than the average z of the 6160 strategies by 2.3 sigma, 2.1 sigma, and 3.8 sigma confidence levels, respectively. The best three TSS values were 0.75 +/- 0.07, 0.48 +/- 0.02, and 0.56 +/- 0.04, respectively.