This article adopts a machine learning method called stacked generalization for forecasting mortality. The main idea is to combine the forecasts from different projection models or algorithms in a certain way in order to increase the prediction accuracy. In particular, the article considers not just the traditionally used mortality projection models, such as the Lee–Carter and CBD models and their extensions, but also some learning algorithms called feedforward and recurrent neural networks that are starting to gain attention in the actuarial literature. For blending the different forecasts, the article examines a number of choices, including simple averaging, weighted averaging, linear regression, and neural network. Using U.S. mortality data, it is found that the proposed stacking approach often outperforms the cases where a projection model or algorithm is applied individually, and that neural networks tend to generate better results than many of the traditional models.

本文采用一种称为堆叠泛化的机器学习方法来预测死亡率。主要思想是将不同投影模型或算法的预测以某种方式结合起来，以提高预测精度。特别是，本文不仅考虑了传统使用的死亡率预测模型，例如 Lee-Carter 和 CBD 模型及其扩展，还考虑了一些称为前馈和循环神经网络的学习算法，这些算法开始在精算文献中受到关注。为了混合不同的预测，本文研究了多种选择，包括简单平均、加权平均、线性回归和神经网络。使用美国死亡率数据，