Time-Series Forecasting of Mortality Rates using Deep Learning

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. Although appealing intuitively, a naive implementation of these networks does not lead to enhanced predictive performance. We show how the structure of the Lee Carter model can be generalized, and propose a relatively simple convolutional network model that can be interpreted as a generalization of the Lee Carter model, allowing for its components to be evaluated in familiar terms. The model produces highly accurate forecasts on the Human Mortality Database, and, without further modification, generalizes well to the United States Mortality Database.