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IAALS Webinar: Point and Interval Forecasts of Death Rates Using Neural Networks


April 12, 2022 / 9:00 am - 10:00 am (EDT)




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 (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN 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 and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN 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 NN model.



Simon Schnürch

Simon Schnürch is a Ph.D. student at the Fraunhofer Institute for Industrial Mathematics (ITWM) and the University of Kaiserslautern, Germany. He holds an M.Sc. in Mathematics and has been working as a scientific consultant in various data science projects with industry partners since 2017. His research interests include longevity risk management and applications of machine learning methods such as cluster analysis and neural networks to mortality modeling and forecasting.


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