Interpretable additive model for analyzing high-dimensional functional time series

High-dimensional functional time series offers a powerful framework for extending functional time series analysis to settings with multiple simultaneous dimensions, capturing both temporal dynamics and cross-sectional dependencies. We propose a novel, interpretable additive model tailored for such data, designed to deliver both high predictive accuracy and clear interpretability. The model features bivariate coefficient surfaces to represent relationships across panel dimensions, with sparsity introduced via penalized smoothing and group bridge regression. This enables simultaneous estimation of the surfaces and identification of significant inter-dimensional effects. Through Monte Carlo simulations and an empirical application to Japanese subnational age-specific mortality rates, we demonstrate the proposed model's superior forecasting performance and interpretability compared to existing functional time series approaches.