Hawkes Process Inference with Missing Data (2018)

by Christian R. Shelton, Zhen Qin, and Chandini Shetty

Abstract: A multivariate Hawkes process is a class of marked point processes: A sample consists of a finite set of events of unbounded random size; each event has a real-valued time and a discrete-valued label (mark). It is self-excitatory: Each event causes an increase in the rate of other events (of either the same or a different label) in the (near) future. Prior work has developed methods for parameter estimation from complete samples.

However, just as unobserved variables can increase the modeling power of other probabilistic models, allowing unobserved events can increase the modeling power of point processes. In this paper we develop a method to sample over the posterior distribution of unobserved events in a multivariate Hawkes process. We demonstrate the efficacy of our approach, and its utility in improving predictive power and identifying latent structure in real-world data.

Download Information

Christian R. Shelton, Zhen Qin, and Chandini Shetty (2018). "Hawkes Process Inference with Missing Data." Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.

Bibtex citation

@inproceedings{SheQinShe18,
   author = "Christian R. Shelton and Zhen Qin and Chandini Shetty",
   title = "{H}awkes Process Inference with Missing Data",
   booktitle = "Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence",
   booktitleabbr = "AAAI",
   year = 2018,
}

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