ABSTRACT

The time-series nature of mortality rates lends itself to processing through neural networks that are specialized to deal with sequential data, such as recurrent and convolutional networks. The aim of this work is to show how the structure of the Lee–Carter model can be generalized using a relatively simple shallow convolutional network model, allowing for its components to be evaluated in familiar terms. Although deep networks have been applied successfully in many areas, we find that deep networks do not lead to an enhanced predictive performance in our approach for mortality forecasting, compared to the proposed shallow one. Our model produces highly accurate forecasts on the Human Mortality Database, and, without further modification, generalizes well to the United States Mortality Database.

摘要

死亡率的时间序列特性使其适合通过专门处理序列数据的神经网络进行处理，例如循环网络和卷积网络。这项工作的目的是展示如何使用相对简单的浅层卷积网络模型来概括 Lee-Carter 模型的结构，从而允许以熟悉的术语评估其组件。尽管深度网络已成功应用于许多领域，但我们发现，与提议的浅层网络相比，深度网络并没有在我们的死亡率预测方法中提高预测性能。我们的模型在人类死亡率数据库上产生高度准确的预测，并且无需进一步修改，就可以很好地推广到美国死亡率数据库。