Is the group structure important in grouped functional time series?

TL;DR

This paper examines whether the grouping structure in functional time series impacts forecast accuracy, using Japanese mortality data and dynamic multivariate models to compare different groupings.

Contribution

It investigates the effect of group structure on forecast accuracy in grouped functional time series, addressing non-uniqueness and practical implications.

Findings

Group structure can influence forecast accuracy

Different disaggregation structures yield varying results

Joint modeling improves forecast performance

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.