RafterNet: Probabilistic predictions in multi-response regression

TL;DR

RafterNet introduces a nonparametric method combining random forests and generative neural networks to make probabilistic predictions in multi-response regression, effectively modeling dependencies between responses.

Contribution

The paper presents a novel approach that integrates random forests with generative neural networks to model dependencies in multi-response regression probabilistically.

Findings

Demonstrates flexibility across multiple datasets

Improves probabilistic forecasting accuracy

Models response dependencies effectively

Abstract

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.