  The contribution of this paper is two-fold. First, we present ProbCast - a
novel probabilistic model for multivariate time-series forecasting. We employ a
conditional GAN framework to train our model with adversarial training. Second,
we propose a framework that lets us transform a deterministic model into a
probabilistic one with improved performance. The motivation of the framework is
to either transform existing highly accurate point forecast models to their
probabilistic counterparts or to train GANs stably by selecting the
architecture of GAN's component carefully and efficiently. We conduct
experiments over two publicly available datasets namely electricity consumption
dataset and exchange-rate dataset. The results of the experiments demonstrate
the remarkable performance of our model as well as the successful application
of our proposed framework.
