用KAN网络提升时间序列因果发现，更准且更可解释。
Granger Causality Detection with Kolmogorov-Arnold Networks
- 用柯尔莫哥洛夫-阿诺德网络替代传统模型，增强非线性建模能力。
- 在高维数据中更精准识别稀疏因果关系，优于MLP模型。
- 适合物理系统动力学规律挖掘与复杂系统因果分析研究者。
在从经济到气候科学的众多领域中，时间序列数据的因果关系发现至关重要。格兰杰因果是强大的因果检测工具，但其原始形式受限于线性假设，近年来才出现基于机器学习的非线性推广。本文通过研究柯尔莫哥洛夫-阿诺德网络（KAN）在格兰杰因果检测中的应用，提出一种名为GC-KAN的框架，并设计了专用于该任务的训练方法。我们在向量自回归（VAR）模型和混沌的Lorenz-96系统上测试该框架，评估了KAN通过识别格兰杰因果关系实现输入特征稀疏化的能力，从而构建简洁而准确的因果模型。结果表明，KAN在高维场景下更擅长捕捉可解释的稀疏格兰杰因果模式，优于多层感知机（MLP），展示了人工智能在揭示物理系统动力学规律方面的潜力。
原文摘要 · Abstract (English)
Discovering causal relationships in time series data is central in many scientific areas, ranging from economics to climate science. Granger causality is a powerful tool for causality detection. However, its original formulation is limited by its linear form and only recently nonlinear machine-learning generalizations have been introduced. This study contributes to the definition of neural Granger causality models by investigating the application of Kolmogorov-Arnold networks (KANs) in Granger causality detection and comparing their capabilities against multilayer perceptrons (MLP). In this work, we develop a framework called Granger Causality KAN (GC-KAN) along with a tailored training approach designed specifically for Granger causality detection. We test this framework on both Vector Autoregressive (VAR) models and chaotic Lorenz-96 systems, analysing the ability of KANs to sparsify input features by identifying Granger causal relationships, providing a concise yet accurate model for Granger causality detection. Our findings show the potential of KANs to outperform MLPs in discerning interpretable Granger causal relationships, particularly for the ability of identifying sparse Granger causality patterns in high-dimensional settings, and more generally, the potential of AI in causality discovery for the dynamical laws in physical systems.
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