A Hybrid Lee–Carter Mortality Prediction Framework with Optimized Fitting Period and Machine Learning Integration

Abstract

Accurate mortality predictions are important for pension sustainability and life insurance valuation. Existing extensions of the Lee–Carter (LC) model typically use a fixed fitting period and rely on a single forecasting approach to predict the time component. This study proposes a hybrid mortality forecasting framework based on the LC model, with a particular focus on improving the estimation of its time component. The approach integrates an optimized selection of the fitting period with both traditional time-series modeling with auto-regressive integrated moving average (ARIMA) (p,d,q) and machine learning techniques, namely artificial neural networks (ANNs) and random forests (RFs). The objective is to assess whether these enhancements improve forecasting performance. Using 45 years of Malaysian age-specific mortality data (1980–2024), this study compares the predictive performance of the standard LC model with the proposed extensions: LC-ARIMA, LC-ANN, and LC-RF. Results showed that, while the LC ARIMA version minimizes prediction residuals by using fitting periods of 1980–2002 for males and 1980–2004 for females, the LC-ANN version achieves the highest aggregate predictive accuracy when averaged across genders. These findings suggest that integrating neural networks into the LC framework effectively captures the time-component patterns. Our projections through 2038 indicate a continuous decline in mortality rates among Malaysians, with greater improvement among females. Overall, the proposed hybrid framework offers a more accurate and flexible approach to mortality forecasting. These improvements are particularly relevant for applications such as pension planning and population projections, where reliable mortality estimates are essential for long-term policy decisions.