Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?

Autor:	Petneházi, Gábor, Gáll, József
Rok vydání:	2019
Předmět:	Quantitative Finance - Risk Management Statistics - Applications Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.05501 Zobrazit plný text záznamu View this record from Arxiv