Granger Causality Pattern Learning Equipped with Noise Invalidation Soft Thresholding
Khashayar Bayati, Karthikeyan Umapathy, Soosan Beheshti · 2023
This paper concentrates on pattern learning of Granger causality. In this context, the entities of the Granger causality matrix estimation derived from the state-space model indicate directional dependencies of the observations. Existing methods propose different forms of thresholding to eliminate insignificant entities from this matrix. These approaches do not exploit the difference in statistical characteristics of insignificant entities compared to the Granger causality itself. In this work, Noise Invalidation Soft Thresholding is chosen as the thresholding method to discard null relations in the Granger causality matrix pattern learning. Unlike existing approaches, the proposed method benefits from the statistical properties of insignificant entities. The simulation results demonstrate the advantages and superiority of the proposed method in the sense of accuracy and robustness for both randomly generated datasets with causal footprints as well as simulated electroencephalogram datasets.