Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
This work addresses causal inference in time series for domains like neuroscience and genomics, but it is incremental as it builds on existing Kolmogorov-Arnold Networks.
The authors tackled the problem of inferring Granger causality from time series by proposing KANGCI, a novel architecture based on Kolmogorov-Arnold Networks, which achieved competitive performance to state-of-the-art methods on various datasets including nonlinear, high-dimensional, and limited-sample scenarios.
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.