Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems

• URL: http://arxiv.org/abs/2509.22395v1
• Date: Fri, 26 Sep 2025 14:22:48 GMT
• Title: Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems
• Authors: Filipe C. L. Duarte, Paulo S. G. de Mattos Neto, Paulo R. A. Firmino,
• Abstract summary: Multi-step-ahead predictions are crucial for public health, demographic planning, and insurance risk assessments.<n>This study evaluated the impact of different multi-step forecasting approaches on the accuracy of hybrid systems.
• Score: 0.9940728137241214
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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