  Prediction intervals are a machine- and human-interpretable way to represent
predictive uncertainty in a regression analysis. In this paper, we present a
method for generating prediction intervals along with point estimates from an
ensemble of neural networks. We propose a multi-objective loss function fusing
quality measures related to prediction intervals and point estimates, and a
penalty function, which enforces semantic integrity of the results and
stabilizes the training process of the neural networks. The ensembled
prediction intervals are aggregated as a split normal mixture accounting for
possible multimodality and asymmetricity of the posterior predictive
distribution, and resulting in prediction intervals that capture aleatoric and
epistemic uncertainty. Our results show that both our quality-driven loss
function and our aggregation method contribute to well-calibrated prediction
intervals and point estimates.
