Granger Causality Detection with Kolmogorov-Arnold Networks

TL;DR

This paper introduces a neural network approach using Kolmogorov-Arnold networks for improved detection of Granger causality in time series, demonstrating superior performance over traditional MLPs especially in high-dimensional, sparse scenarios.

Contribution

It develops the GC-KAN framework and a specialized training method, advancing neural Granger causality detection with a focus on interpretability and sparsity in complex systems.

Findings

KANs outperform MLPs in identifying sparse causal relationships

GC-KAN effectively models causality in high-dimensional systems

AI methods can uncover physical laws from dynamical data

Abstract

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.