JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1031 10.6339/21-JDS1031 Statistical Data Science Is the Group Structure Important in Grouped Functional Time Series? https://orcid.org/0000-0002-8323-1490 YangYang1 https://orcid.org/0000-0003-1769-6430 ShangHan Linhanlin.shang@mq.edu.au2∗ Department of Econometrics and Business Statistics, Monash University, Australia Department of Actuarial Studies and Business Analytics, Level 7, 4 Eastern Road, Macquarie University, Sydney, NSW 2109, Australia Corresponding author. Email: hanlin.shang@mq.edu.au. 2022412022203303324 Supplementary Material

For reproducibility, all R codes are collated in a Github repository at https://github.com/hanshang/GMFTS. This repository contains the following files:

README: Describe the functionality of each R file.

R files with indices 1 to 29: Functions used to compute numerical results presented in the paper.

Example: Contains a quick example of forecasting prefecture-level age-specific mortality rates using the DMFTS method.

30820218112021 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

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.

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