Node correlation causal inference of spatio-temporal association data based on Bayesian method

Abstract

Spatio-temporal association data refers to spatial feature information flows that have spatio-temporal associations with each other and change over time. The mining of spatio-temporal related data has attracted much attention and has been widely used in many fields. At present, many researches of spatio-temporal data mining can only infer the association mode of spatio-temporal objects from the system. Therefore, based on the complex network theory, this paper proposed a spatio-temporal data networked node correlation inference method based on Bayesian method. Based on the network topology structure, the sample statistics method was designed according to time and space information. The conditional probability between network nodes was calculated so that it was applied to the node correlation causal inference. Finally, the method was applied to two typical spatio-temporal correlation data networks - seismic network and pest-relational network, and the prediction accuracy was 76% and 80%, respectively. In addition, this paper analyzed the relationship between the accuracy of the prediction and other factors such as test set size, structural core and magnitude limits.