[PEM12-P04] Solar Flare Prediction using the Machine-learning and Operational Evaluation Method
Keywords:Solar Flare , Prediction, Modeling , Machine-learning, Evaluation Method
In this presentation, we introduce a method of the time-series cross-validation (CV) to evaluate flare prediction models in an operational setting, though the k-fold (10-fold) CV has ever been used in the previous studies. In some sense, these two methods are reasonable and available. However, when we focus on the operational usage, the time-series CV is superior to the k-fold CV. Furthermore, we used a machine-learning algorithm called the Gradient Boosted Trees for the first time. The boosting is a method to minimize the loss function by sequentially adding weak classifiers, or decision trees in our model. This is used to achieve a better prediction, by repeating learning of the calculation of the gradient when optimizing parameters in each step. We applied this algorithm to the flare prediction and performed the time-series CV. As a result, we succeeded in improving our prediction score, a skill score called the true skill statistic, from 0.2 to 0.6 for X-class flares and to 0.8 for M-class flares. We also compared the performance of other five different machine-learning algorithms to predict flares, and we found that the ranking of the performance of the algorithms completely differs according to the CV method.