Point and Interval Forecasts of Death Rates Using Neural Networks

The Lee-Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network architecture for mortality rate forecasting, empirically compare this model as well as other neural network models to the Lee-Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors, forecasts and global behavior of our model to make it more understandable and, thus, more trustworthy. As neural networks by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our neural network model