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FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY
ASTIN Bulletin: The Journal of the IAA ( IF 1.7 ) Pub Date : 2020-02-18 , DOI: 10.1017/asb.2020.3
Han Lin Shang , Steven Haberman

When modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; (2) how to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure; (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.

中文翻译:

预测群体结构中的多个功能时间序列:对死亡率的应用

在对地方死亡率进行建模时,我们应考虑以下三个特征:(1)如何将子种群之间的任何可能相关性纳入其中,以通过多种群联合建模潜在地提高预测准确性;(2)如何调和国家以下地方的死亡率预测,以便它们在群体结构的各个层次上得到适当的汇总;(3)在预测对账方法中,如何组合其预测以提高预测精度。为了解决这些问题,我们引入了分组单变量函数时间序列方法的扩展。我们首先考虑使用多元函数时间序列方法来共同预测多个相关序列。然后,我们评估在预测对帐方法之间使用预测组合的影响和收益。
更新日期:2020-02-18
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