<ul><li>Discovering causal relationships in time series data is central in many scientific areas.</li><li>Granger causality is a powerful tool for causality detection, but its original formulation is limited to linear relationships.</li><li>This study explores the use of Kolmogorov-Arnold networks (KANs) in Granger causality detection, comparing them to multilayer perceptrons (MLP).</li><li>The findings suggest that KANs have the potential to outperform MLPs in identifying sparse Granger causal relationships, especially in high-dimensional settings.</li></ul>

Granger Causality Detection with Kolmogorov-Arnold Networks

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