  Generative Adversarial Networks (GANs) have gained significant attention in
recent years, with impressive applications highlighted in computer vision in
particular. Compared to such examples, however, there have been more limited
applications of GANs to time series modelling, including forecasting. In this
work, we present the Mixture Density Conditional Generative Adversarial Model
(MD-CGAN), with a focus on time series forecasting. We show that our model is
capable of estimating a probabilistic posterior distribution over forecasts and
that, in comparison to a set of benchmark methods, the MD-CGAN model performs
well, particularly in situations where noise is a significant component of the
observed time series. Further, by using a Gaussian mixture model as the output
distribution, MD-CGAN offers posterior predictions that are non-Gaussian.
