  A fully nonparametric approach for making probabilistic predictions in
multi-response regression problems is introduced. Random forests are used as
marginal models for each response variable and, as novel contribution of the
present work, the dependence between the multiple response variables is modeled
by a generative neural network. This combined modeling approach of random
forests, corresponding empirical marginal residual distributions and a
generative neural network is referred to as RafterNet. Multiple datasets serve
as examples to demonstrate the flexibility of the approach and its impact for
making probabilistic forecasts.
