# Prediction intervals for neural network models using weighted asymmetric loss functions

arXiv: https://arxiv.org/abs/2210.04318 · HTML: https://mlsanity.com/paper/2210.04318/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [SGD Converges to Global Minimum in Deep Learning via Star-convex Path](https://mlsanity.com/paper/1901.00451/) (2019)
- [The convergence of the Stochastic Gradient Descent (SGD) : a self-contained proof](https://mlsanity.com/paper/2103.14350/) (2021)

## Abstract (arXiv)

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.