Sociology

Sample (material)

Population

Population model

Politics

Network model

Machine learning

Demography

Political science

Econometrics

Artificial intelligence

Economics

Chemistry

Programming language

Extension (predicate logic)

Representation (politics)

Computer science

Chromatography

Artificial neural network

Annals of Actuarial Science

The Lee–Carter (LC) model is a basic approach to forecasting mortality rates of a single population. Although extensions of the LC model to forecasting rates for multiple populations have recently been proposed, the structure of these extended models is hard to justify and the models are often difficult to calibrate, relying on customised optimisation schemes. Based on the paradigm of representation learning, we extend the LCmodel to multiple populations using neural networks, which automatically select an optimal model structure. We fit this model to mortality rates since 1950 for all countries in the Human Mortality Database and observe that the out-of-sample forecasting performance of the model is highly competitive.

A neural network extension of the Lee–Carter model to multiple populations