Interpretable additive model for analyzing high-dimensional functional time series

2025/04/28 by Haixu Wang, Wang, Haixu, Tianyu Guan +3 · 1 voice
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Computation (stat.CO) #FOS: Computer and information sciences #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Methods and Inference #stat.CO #stat.ME

paper · pdf · doi:10.48550/arxiv.2504.19904

openalex publication_date 2025/04/28 · arxiv published 2025/04/28 · openalex created_date 2025/10/10 · arxiv updated 2025/12/05 · openalex updated_date 2026/08/01

Abstract

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.

Citations

• Dependence-based fuzzy clustering of functional time series
• Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures
• Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality
• Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series
• Clustering and Forecasting Multiple Functional Time Series
• Factor modelling for high-dimensional functional time series
• Forecasting high-dimensional functional time series with dual-factor structures
• Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces
• On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions
• Statistical Inference for High Dimensional Panel Functional Time Series
• High-dimensional functional time series forecasting: An application to age-specific mortality rates
• Functional linear regression with points of impact
• Functional additive regression
• Functional Regression
• Functional Regression
• Shrinkage Estimation and Selection for Multiple Functional Regression
• Fractals with point impact in functional linear regression
• Variable selection in nonparametric additive models
• Functional linear regression that's interpretable

Discussions

• 📈🤖 Interpretable additive model for analyzing high-dimensional functional time series. Wang, Guan, and Shang. High-dimensional functional time series offers a powerful framework for extending functi [bsky, 0 points, 0 comments]

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• Forecasting high-dimensional functional time series with dual-factor structures
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