StateHPs: State Hawkes processes for Granger causal discovery from non-stationary event sequences
Liu, Yuequn Sun, Guangdong Cai, Ruichu Li, Zijian Zhang, Keli Pan, Lujia Hao, Zhifeng
Liu, Yuequn
Sun, Guangdong
Cai, Ruichu
Li, Zijian
Zhang, Keli
Pan, Lujia
Hao, Zhifeng
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Learning Granger causality from event sequences has important applications in various scenarios. Many methods have been developed based on Hawkes process with a stationarity assumption. However, these methods often fail in real-world scenarios due to violating the stationarity assumption, as an event sequence can be generated under different states at varying times. Although some work tries to model non-stationarity by searching for best segmentation, they still suffer from the lack of robustness and identification guarantee. An intuitive solution is to model the non-stationary generation process in a unified probabilistic generative framework. This presents two significant challenges: how to model the generation process considering both the stationarity of each subsequence and the non-stationarity among the subsequences, and how to identify the Granger causality. To address these challenges, we devise State Hawkes Processes (StateHPs). For the first challenge, StateHPs formulates the state assignments of each subsequence as a Dirichlet distribution and each state as a Hawkes process. For the second challenge, StateHPs introduces a variational Expectation-Maximization algorithm to identify the Granger causal graph. We also develop the identification theories for StateHPs. On real-world data, StateHPs achieves 35.5%, 33.9%, and 36.7% improvement among F1, Precision, and Recall metrics compared to the SOTA baselines. © 2025
Citation
Y. Liu et al., “StateHPs: State Hawkes processes for Granger causal discovery from non-stationary event sequences,” Inf Sci (N Y), vol. 718, p. 122422, Nov. 2025, doi: 10.1016/J.INS.2025.122422
Source
Information Sciences
Conference
Keywords
Granger causalityHawkes processNon-stationarityVariational expectation-maximization, Granger causality, Hawkes process, Non-stationarity, Variational expectation-maximization
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Publisher
Elsevier