在欧洲人口集群中提前预测死亡率:整合深度学习和多尺度分析
Yuewen Shen1, Xinhao Yang2, Hao Liu1
1School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China.
Scientific reports
|March 16, 2024
概括
预测人口死亡率对于保险和经济政策至关重要. 一种具有适应性调整 (GT-A) 的新图形变压器模型显著提高了死亡率预测的准确性,而不是传统方法.
科学领域:
- 人口统计学 人口统计学
- 精算科学 精算科学
- 计算统计学 计算统计学
背景情况:
- 准确的人口死亡率预测对于退休保险和经济政策至关重要.
- 深度学习时间序列预测 (DLTSF) 显示出对传统模型如Lee-Carter (LC) 的承诺.
研究的目的:
- 开发一种先进的模型,用于预测欧洲大人口集群中的死亡率.
- 整合和分析来自多个国家的高维死亡率数据的年龄特征.
主要方法:
- 主要组件分析 (PCA) 用于维度缩小和统计集群.
- 开发一个适应性调整矩阵,包括顺序和空间地理信息.
- 混合模型结合了图形神经网络 (GNN) 和变压器网络 (GT-A模型).
主要成果:
- 拟议的GT-A模型与LC和其他神经网络相比,显示出更高的预测准确性.
- 使用人类死亡数据库数据的验证证实了该模型的有效性.
- 该模型成功地捕获了死亡率数据的时空特征和异质特征.
结论:
- GT-A模型为全球人口研究提供了强大而准确的预测工具.
- 这一进步对国际人寿保险行业和精算科学产生了重大影响.
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