RafterNet: Probabilistic Predictions in Multi-Response Regression

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.

Keywords:

• Copulas
• Generative neural networks
• Learning distributions
• Multi-response regression
• Probabilistic forecasts
• Random forests

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

Marius Hofert acknowledges support from NSERC (grant RGPIN-2020-04897). Marius Hofert acknowledges support from Fin-ML CREATE scholarship. Mu Zhu acknowledges support from NSERC (RGPIN-2016-03876).