Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?
arXiv:1909.05501v211 citations
AI Analysis
This work addresses mortality forecasting for demographers and actuaries, but appears incremental as it applies an existing neural network method to a known problem.
The authors tackled mortality rate forecasting by applying a long short-term memory recurrent neural network (LSTM) that can be trained jointly on data from multiple countries, ages, and sexes, and found that it outperforms the popular Lee-Carter model.
This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the popular Lee-Carter model.