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Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Hongteng Xu · Dixin Luo · Hongyuan Zha

Tue Aug 08 08:48 PM -- 09:06 PM (PDT) @ C4.6 & C4.7

Many real-world applications require robust algorithms to learn point process models based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences. In this paper, we study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the general idea of data synthesis. In particular, given SDC event sequences observed in a variety of time intervals, we propose a sampling-stitching data synthesis method --- sampling predecessor and successor for each SDC event sequence from potential candidates and stitching them together to synthesize long training sequences. The rationality and the feasibility of our method are discussed in terms of arguments based on likelihood. Experiments on both synthetic and real-world data demonstrate that the proposed data synthesis method improves learning results indeed for both time-invariant and time-varying Hawkes processes.

Author Information

Hongteng Xu (Georgia Institute of Technology)
Dixin Luo (University of Toronto)
Hongyuan Zha (Georgia Institute of Technology)

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