Prediction interval for neural network models using weighted asymmetric loss functions. (arXiv:2210.04318v1 [stat.ML])

We develop a novel and simple method to produce prediction intervals (PIs)
for fitting and forecasting exercises. It finds the lower and upper bound of
the intervals by minimising a weighted asymmetric loss function, where the
weight depends on the width of the interval. We give a short mathematical
proof. As a corollary of our proof, we find PIs for values restricted to a
parameterised function and argue why the method works for predicting PIs of
dependent variables. The results …

arxiv interval loss network neural network prediction