论文标题
使用加权不对称损耗功能的神经网络模型的预测间隔
Prediction intervals for neural network models using weighted asymmetric loss functions
论文作者
论文摘要
我们提出了一种简单有效的方法来生成近似和预测趋势的预测间隔（PI）。我们的方法利用加权的不对称损耗函数来估计PI的下限和上限，并由其覆盖率概率确定。我们提供了该方法的简洁数学证明，展示如何将其扩展到用于参数函数的PI，并在训练深层神经网络时讨论其有效性。使用基于神经网络的模型在现实世界预测任务上进行了该方法的测试表明，它可以在复杂的机器学习方案中产生可靠的PI。
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.
