RafterNet: Probabilistic predictions in multi-response regression
This work addresses the need for nonparametric probabilistic forecasting in multi-response regression, which is incremental as it integrates existing methods in a novel way.
The paper tackled the problem of making probabilistic predictions in multi-response regression by introducing RafterNet, which combines random forests for marginal models with a generative neural network to model dependencies between responses, demonstrating flexibility and impact across multiple datasets.
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.