  One of the limiting factors in training data-driven, rare-event prediction
algorithms is the scarcity of the events of interest resulting in an extreme
imbalance in the data. There have been many methods introduced in the
literature for overcoming this issue; simple data manipulation through
undersampling and oversampling, utilizing cost-sensitive learning algorithms,
or by generating synthetic data points following the distribution of the
existing data. While synthetic data generation has recently received a great
deal of attention, there are real challenges involved in doing so for
high-dimensional data such as multivariate time series. In this study, we
explore the usefulness of the conditional generative adversarial network (CGAN)
as a means to perform data-informed oversampling in order to balance a large
dataset of multivariate time series. We utilize a flare forecasting benchmark
dataset, named SWAN-SF, and design two verification methods to both
quantitatively and qualitatively evaluate the similarity between the generated
minority and the ground-truth samples. We further assess the quality of the
generated samples by training a classical, supervised machine learning
algorithm on synthetic data, and testing the trained model on the unseen, real
data. The results show that the classifier trained on the data augmented with
the synthetic multivariate time series achieves a significant improvement
compared with the case where no augmentation is used. The popular flare
forecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold
improvements, respectively, indicating the remarkable statistical similarities,
and the usefulness of CGAN-based data generation for complicated tasks such as
flare forecasting.
