A KAN-based framework with a first-layer proximal operator detects Granger causal relationships in simulated VAR and Lorenz-96 time series, with accuracy comparable to cMLP baselines.
Each time series generated has self dependencies and three ran- domly selected parents among the other n−1 series
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Granger Causality Detection with Kolmogorov-Arnold Networks
A KAN-based framework with a first-layer proximal operator detects Granger causal relationships in simulated VAR and Lorenz-96 time series, with accuracy comparable to cMLP baselines.