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 language | English |
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Pages (from-to) | 303-324 |
Number of pages | 22 |
Journal | Journal of Data Science |
Volume | 20 |
Issue number | 3 |
Early online date | 4 Jan 2022 |
DOIs | |
Publication status | Published - 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