  Time series forecasting is one of the challenging problems for humankind.
Traditional forecasting methods using mean regression models have severe
shortcomings in reflecting real-world fluctuations. While new probabilistic
methods rush to rescue, they fight with technical difficulties like quantile
crossing or selecting a prior distribution. To meld the different strengths of
these fields while avoiding their weaknesses as well as to push the boundary of
the state-of-the-art, we introduce ForGAN - one step ahead probabilistic
forecasting with generative adversarial networks. ForGAN utilizes the power of
the conditional generative adversarial network to learn the data generating
distribution and compute probabilistic forecasts from it. We argue how to
evaluate ForGAN in opposition to regression methods. To investigate
probabilistic forecasting of ForGAN, we create a new dataset and demonstrate
our method abilities on it. This dataset will be made publicly available for
comparison. Furthermore, we test ForGAN on two publicly available datasets,
namely Mackey-Glass dataset and Internet traffic dataset (A5M) where the
impressive performance of ForGAN demonstrate its high capability in forecasting
future values.
