Is the group structure important in grouped functional time series?

Yang Yang, Han Lin Shang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
33 Downloads (Pure)

Abstract

We study the importance of group structure in grouped functional time series. Due to the non-uniqueness of group structure, we investigate different disaggregation structures in grouped functional time series. We address a practical question on whether or not the group structure can affect forecast accuracy. Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures.
Original languageEnglish
Pages (from-to)303-324
Number of pages22
JournalJournal of Data Science
Volume20
Issue number3
Early online date4 Jan 2022
DOIs
Publication statusPublished - Jul 2022

Keywords

  • dynamic principal component analysis
  • forecast reconciliation
  • Japanese sub-national age-specific mortality rates
  • long-run covariance function
  • multivariate functional principal component analysis

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