Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems
It addresses forecasting challenges for insurance and pension markets, but is incremental as it builds on existing hybrid methods.
This study tackled the problem of accurate mortality rate forecasting with limited data by evaluating multi-step approaches and ML models in hybrid systems, finding that an ARIMA-LSTM hybrid with a recursive approach outperformed others in most cases across 12 datasets and 21 models.
The decline in interest rates and economic stabilization has heightened the importance of accurate mortality rate forecasting, particularly in insurance and pension markets. Multi-step-ahead predictions are crucial for public health, demographic planning, and insurance risk assessments; however, they face challenges when data are limited. Hybrid systems that combine statistical and Machine Learning (ML) models offer a promising solution for handling both linear and nonlinear patterns. This study evaluated the impact of different multi-step forecasting approaches (Recursive, Direct, and Multi-Input Multi-Output) and ML models on the accuracy of hybrid systems. Results from 12 datasets and 21 models show that the selection of both the multi-step approach and the ML model is essential for improving performance, with the ARIMA-LSTM hybrid using a recursive approach outperforming other models in most cases.