用KAN网络推断时间序列因果关系，更准且能自动筛选真因果。
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
- 基于KAN的权重提取与正则化设计，提升非线性时间序列因果推断能力。
- 在洛伦兹-96、基因调控等5类数据上表现优于现有方法。
- 结合时间反演检测，有效减少虚假因果连接，适合小样本高维数据。
我们提出格兰杰因果推断的科莫戈罗夫-阿诺德网络（KANGCI），一种将近期提出的科莫戈罗夫-阿诺德网络（KAN）扩展至因果推断领域的新型架构。通过从KAN层中提取基础权重，并引入稀疏诱导惩罚和岭正则化，KANGCI能有效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演格兰杰因果的算法，可自动选择来自原始或时间反演序列的因果关系，或融合结果以缓解虚假连通性问题。在洛伦兹-96、基因调控网络、fMRI BOLD信号、向量自回归（VAR）及真实世界脑电图（EEG）数据集上的综合实验表明，该模型在非线性、高维及小样本时间序列中推断格兰杰因果关系方面，性能媲美当前最优方法。
原文摘要 · Abstract (English)
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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