First Steps Toward Synthetic Sample Generation for Machine Learning Based Flare Forecasting

The imbalanced class problem is intrinsic to solar flare forecasting, as are other issues we find in data-driven forecasting problems that are often hidden within an imbalanced dataset. One method of dealing with imbalanced data is to balance the data by using synthetic oversampling to create synthetic examples of the minority class. Though synthetic oversampling techniques have been applied to problems in medicine, finance, security, and other areas, we have not seen these approaches used in solar flare forecasting. We investigate two methods of synthetic oversampling, Rapidly Converging Gibbs Sampler (RACOG) and Synthetic Minority Oversampling Technique (SMOTE). We devise three naive synthetic oversampling techniques for comparison. We rely on data provided by the Space Weather ANalytics for Solar Flares (SWAN- SF) benchmark dataset. Our results indicate that synthetic oversampling can be effective for machine learning based solar flare forecasting.