Forecasting multiple functional time series in a group structure: An application to mortality

Han Lin Shang*, Steven Haberman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)357-379
Number of pages23
JournalASTIN Bulletin
Issue number2
Publication statusPublished - 18 May 2020
Externally publishedYes


  • Forecast reconciliation
  • multivariate functional principal component analysis
  • bottom-up method
  • optimal combination method
  • Japanese mortality database


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