MultiFun-DAG: Multivariate Functional Directed Acyclic Graph
Abstract
Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex systems under a facile and oversimplified form. This paper considers that nodes can be multivariate functional data and thus proposes a multivariate functional DAG (MultiFun-DAG). It constructs a hidden bilinear multivariate function-to-function regression to describe the causal relationships between different nodes. Then an Expectation-Maximum algorithm is used to learn the graph structure as a score-based algorithm with acyclic constraints. Theoretical properties are diligently derived. Prudent numerical studies and a case study from urban traffic congestion analysis are conducted to show MultiFun-DAG's effectiveness.
Cite
@article{arxiv.2404.13836,
title = {MultiFun-DAG: Multivariate Functional Directed Acyclic Graph},
author = {Tian Lan and Ziyue Li and Junpeng Lin and Zhishuai Li and Lei Bai and Man Li and Fugee Tsung and Rui Zhao and Chen Zhang},
journal= {arXiv preprint arXiv:2404.13836},
year = {2024}
}