混合模型结合递归预测，显著提升小样本死亡率预测精度
Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems
- 采用统计模型与机器学习融合的递归预测方法
- 在12个数据集上，ARIMA-LSTM递归组合误差最低
- 适合保险、养老金等需长期风险评估的场景
利率下行与经济稳定使死亡率精准预测愈发重要，尤其在保险与养老金领域。多步预测对公共卫生、人口规划和保险风险评估至关重要，但在数据有限时面临挑战。结合统计与机器学习（ML）的混合系统可有效捕捉线性与非线性模式。本研究评估了递归、直接及多输入多输出三种多步预测方法，以及21种不同模型在12个数据集上的表现。结果表明，多步预测方法与机器学习模型的选择对性能影响重大；其中，使用递归策略的ARIMA-LSTM混合模型在多数情况下表现最优。
原文摘要 · Abstract (English)
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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