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arXiv · 2501.18221

Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network

Elvira Romano·Antonio Irpino·Claire Miller
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Abstract

In this paper, we propose a Network-Weighted Functional Regression (NWFR) model, an extension of Spatially Weighted Functional Regression (SWFR) to functional data defined on network-structured settings. To asses predictive uncertainity, we develop a functional conformal prediction procedure that yields a distribution free prediction intervals with guaranteed coverage. Through extensive evaluation on both simulated and real-world datasets, we demonstrate that the explicit modeling of network structure yields substantive improvements in point-prediction accuracy and markedly enhances the validity and precision of the resulting prediction intervals.

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Elvira Romano, Antonio Irpino, Claire Miller. 2025-01-30. Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network. https://arxiv.org/abs/2501.18221

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