融合先验约束的拓扑霍克斯过程 格兰杰因果发现算法.

Granger causality discovery algorithm for discrete-time series data has important application value. The existing method smainly use Hawkes processes modeling, which cannot be applied to non-IID data and data with time-skew errors. Therefore, this paper proposed a Granger causality discovery algorithm (PTHP) for topological Hawkes processes integrating a priori constraints. Firstly, it used the constraint-based method to screen a group of causal edges with a high significance level to improve the tolerance of the algorithm to the fault time-skew errors. Then, the edges obtained in the previous step were fused into the topological Hawkes processes as a priori constraints to solve the non-IID problem between sequences. Experiments on simulted data and real-world data show the effectiveness of this method, and it won first place in PCIC 2021 causal inference competition. [ABSTRACT FROM AUTHOR]