Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Abstract

We proposed a method to extract causal relations of spatial clusters from multi-dimensional event sequence data, also known as a spatio-temporal point process. The proposed Granger cluster sequence mining algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extended the cluster sequence mining algorithm, which utilized a statistical inference technique to identify the occurrence relation, with a causality inference based on the Granger causality. In addition, the proposed method utilizes a false discovery rate procedure to control the significance of the causality. Based on experiments on both synthetic and semi-real data, we confirmed that the algorithm is able to extract the synthetic causal relations from multiple different sets of data, even when disturbed with high level of spatial noise. False discovery rate procedure also helps to increase the accuracy even more under such case and also make the algorithm less-sensitive to the hyperparameters.