One of the main challenges for life actuaries is modeling and predicting
the future mortality evolution. To this end, several stochastic mortality models have
been proposed in literature, starting from the pivotal approach of the Lee-Carter
model. These models essentially use the ARIMA processes to forecast the future
mortality trends. Recently, some research works have shown the adequacy of the
deep learning techniques to improve mortality modeling, obtaining competitive and
outperforming forecasts compared to the ARIMA. The present work focuses on the
application of a recurrent neural network, the Long Short-Term Memory (LSTM),
in the Lee-Carter model framework. The LSTM has an architecture specifically designed
to model and predict sequential data, such as time series, well capturing
hidden patterns within data related to events that may be far from each other. In
mortality modeling, this means that the forecasted mortality rates take into account
the hidden features of the past phenomenon not always adequately captured by the
ARIMA.We extend the approach proposed in Nigri et al. (2019), performing a point
forecasting of the Lee-Carter time-index through LSTM and deriving the related
prediction interval representing the LSTM’s parameter uncertainty.
