Lee and Carter go Machine Learning: Recurrent Neural Networks

30 Pages Posted: 23 Aug 2019 Last revised: 29 Aug 2019

See all articles by Ronald Richman

Ronald Richman

Old Mutual Insure; University of the Witwatersrand

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: August 22, 2019


In this tutorial we introduce recurrent neural networks (RNNs), and we describe the two most popular RNN architectures. These are the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. Their common field of application is time series modeling, and we demonstrate their use on a mortality rate prediction problem using data from the Swiss female and male populations.

Keywords: recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), Lee-Carter (LC) model, mortality forecasting, feed-forward neural network (FNN)

JEL Classification: C14, C23, C38, G22, J10

Suggested Citation

Richman, Ronald and Wuthrich, Mario V., Lee and Carter go Machine Learning: Recurrent Neural Networks (August 22, 2019). Available at SSRN: https://ssrn.com/abstract=3441030 or http://dx.doi.org/10.2139/ssrn.3441030

Ronald Richman

Old Mutual Insure ( email )

Wanooka Place
St Andrews Road
Johannesburg, 2192
South Africa

University of the Witwatersrand ( email )

1 Jan Smuts Avenue
Johannesburg, GA Gauteng 2000
South Africa

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics