CGAN-based synthetic multivariate time-series generation: a solution to data scarcity in solar flare forecasting

One of the major bottlenecks in refining supervised algorithms is data scarcity. This might be caused by a number of reasons often rooted in extremely expensive and lengthy data collection processes. In natural domains such as Heliophysics, it may take decades for sufficiently large samples for machine learning purposes. Inspired by the massive success of generative adversarial networks (GANs) in generating synthetic images, in this study we employed the conditional GAN (CGAN) on a recently released benchmark dataset tailored for solar flare forecasting. Our goal is to generate synthetic multivariate time-series data that (1) are statistically similar to the real data and (2) improve the performance of flare prediction when used to remedy the scarcity of strong flares. To evaluate the generated samples, first, we used the Kullback-Leibler divergence and adversarial accuracy measures to quantify the similarity between the real and synthetic data in terms of their descriptive statistics. Second, we evaluated the impact of the generated samples by training a predictive model on their descriptive statistics, which resulted in a significant improvement (over 1100% in TSS and 350% in HSS). Third, we used the generated time series to examine their high-dimensional contribution to mitigating the scarcity of the strong flares, which we also observed a significant improvement in terms of TSS (4%, 7%, and 31%) and HSS (75%, 35%, and 72%), compared to oversampling, undersampling, and synthetic oversampling methods, respectively. We believe our findings can open new doors toward more robust and accurate flare forecasting models.