Prediction intervals for neural network models using weighted asymmetric loss functions
This work addresses the need for uncertainty quantification in neural network predictions, but it appears incremental as it builds on existing loss function methods.
The authors tackled the problem of generating prediction intervals for neural network models by proposing a weighted asymmetric loss function approach, which produced reliable intervals in a real-world forecasting task.
We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical proof of the method, show how it can be extended to derive PIs for parametrised functions and discuss its effectiveness when training deep neural networks. The presented tests of the method on a real-world forecasting task using a neural network-based model show that it can produce reliable PIs in complex machine learning scenarios.