Learning Hawkes Processes Under Synchronization Noise

Multivariate Hawkes processes (MHP) are widely used in a variety of fields to model the occurrence of discrete events. Prior work on learning MHPs has only focused on inference in the presence of perfect traces without noise. We address the problem of learning the causal structure of MHPs when observations are subject to an unknown delay. In particular, we introduce the so-called synchronization noise, where the stream of events generated by each dimension is subject to a random and unknown time shift. We characterize the robustness of the classic maximum likelihood estimator to synchronization noise, and we introduce a new approach for learning the causal structure in the presence of noise. Our experimental results show that our approach accurately recovers the causal structure of MHPs for a wide range of noise levels, and significantly outperforms classic estimation methods.


Editor(s):
Chaudhuri, Kamalika
Salakhutdinov, Ruslan
Published in:
Proceedings of the 36th International Conference on Machine Learning, 97, 6325--6334
Presented at:
36th International Conference on Machine Learning, Long Beach, California, USA, June 9-15, 2019
Year:
Jun 09 2019
Publisher:
PMLR
Keywords:
Laboratories:




 Record created 2019-11-04, last modified 2019-12-05

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