Is the Group Structure Important in Grouped Functional Time Series?

Journal of Data Science

article

Yang Yang¹  Han Lin Shang² 

[1] Department of Econometrics and Business Statistics, Monash University;Department of Actuarial Studies and Business Analytics, Level 7, 4 Eastern Road, Macquarie University

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

DOI  :  10.6339/21-JDS1031

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

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.

【 授权许可】

CC BY