Kolmogorov-Arnold Networks for Time Series Granger Causality Inference

• URL: http://arxiv.org/abs/2501.08958v2
• Date: Wed, 05 Feb 2025 15:26:49 GMT
• Title: Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
• Authors: Meiliang Liu, Yunfang Xu, Zijin Li, Zhengye Si, Xiaoxiao Yang, Xinyue Yang, Zhiwen Zhao,
• Abstract summary: We propose a novel architecture that extends the recently proposed Kolmogorov-Arnold Networks (KAN) to the domain of causal inference. By extracting base weights from KAN layers, KANGCI effectively infers the Granger causality from time series. We also propose an algorithm that automatically selects causal relationships with better inference performance from the original or time-reversed time series.
• Score: 1.3401966602181168
• License:
• Abstract: We propose the Granger causality inference Kolmogorov-Arnold Networks (KANGCI), a novel architecture that extends the recently proposed Kolmogorov-Arnold Networks (KAN) to the domain of causal inference. By extracting base weights from KAN layers and incorporating the sparsity-inducing penalty and ridge regularization, KANGCI effectively infers the Granger causality from time series. Additionally, we propose an algorithm based on time-reversed Granger causality that automatically selects causal relationships with better inference performance from the original or time-reversed time series or integrates the results to mitigate spurious connectivities. Comprehensive experiments conducted on Lorenz-96, Gene regulatory networks, fMRI BOLD signals, VAR, and real-world EEG datasets demonstrate that the proposed model achieves competitive performance to state-of-the-art methods in inferring Granger causality from nonlinear, high-dimensional, and limited-sample time series.

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