  We introduce a new category of multivariate conditional generative models and
demonstrate its performance and versatility in probabilistic time series
forecasting and simulation. Specifically, the output of quantile regression
networks is expanded from a set of fixed quantiles to the whole Quantile
Function by a univariate mapping from a latent uniform distribution to the
target distribution. Then the multivariate case is solved by learning such
quantile functions for each dimension's marginal distribution, followed by
estimating a conditional Copula to associate these latent uniform random
variables. The quantile functions and copula, together defining the joint
predictive distribution, can be parameterized by a single implicit generative
Deep Neural Network.
