用于时间序列格兰杰因果推断的Kolmogorov-Arnold网络
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
AI总结：
本文提出KANGCI模型，将KAN扩展至格兰杰因果推断，结合稀疏惩罚与岭正则化，并利用时间反转算法自动选择或整合因果结果，在多个非线性高维时间序列数据集上取得与最先进方法相当的性能。
摘要：
我们提出格兰杰因果推断Kolmogorov-Arnold网络（KANGCI），这是一种新颖的架构，将最近提出的Kolmogorov-Arnold网络（KAN）扩展到因果推断领域。通过从KAN层中提取基础权重，并结合稀疏诱导惩罚和岭正则化，KANGCI能够有效地从时间序列中推断格兰杰因果关系。此外，我们提出一种基于时间反转格兰杰因果的算法，该算法自动从原始或时间反转时间序列中选择具有更好推断性能的因果关系，或整合结果以减轻虚假连接。在Lorenz-96、基因调控网络、fMRI BOLD信号、VAR和真实世界EEG数据集上进行的综合实验表明，所提出的模型在从非线性、高维和有限样本时间序列中推断格兰杰因果关系方面达到了与最先进方法相当的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
