Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916598272491520 |
|---|---|
| author | Liu, Meiliang Xu, Yunfang Li, Zijin Si, Zhengye Yang, Xiaoxiao Yang, Xinyue Zhao, Zhiwen |
| author_facet | Liu, Meiliang Xu, Yunfang Li, Zijin Si, Zhengye Yang, Xiaoxiao Yang, Xinyue Zhao, Zhiwen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kolmogorov-Arnold Networks for Time Series Granger Causality Inference Liu, Meiliang Xu, Yunfang Li, Zijin Si, Zhengye Yang, Xiaoxiao Yang, Xinyue Zhao, Zhiwen Machine Learning Artificial Intelligence 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. |
| title | Kolmogorov-Arnold Networks for Time Series Granger Causality Inference |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.08958 |