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

If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN