TY - GEN
T1 - Learning hawkes processes from short doubly-censored event sequences
AU - Xu, Hongteng
AU - Luo, Dixin
AU - Zha, Hongyuan
N1 - Publisher Copyright:
© Copyright 2017 by the authors(s).
PY - 2017
Y1 - 2017
N2 - Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data - the so-called short doubly-censored (SDC) event sequences. We study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the idea of data synthesis. Given SDC event sequences observed in a variety of time intervals, we propose a sampling-stitching data synthesis method, sampling predecessors and successors 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.
AB - Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data - the so-called short doubly-censored (SDC) event sequences. We study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the idea of data synthesis. Given SDC event sequences observed in a variety of time intervals, we propose a sampling-stitching data synthesis method, sampling predecessors and successors 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.
UR - http://www.scopus.com/inward/record.url?scp=85047008639&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85047008639
T3 - 34th International Conference on Machine Learning, ICML 2017
SP - 5843
EP - 5863
BT - 34th International Conference on Machine Learning, ICML 2017
PB - International Machine Learning Society (IMLS)
T2 - 34th International Conference on Machine Learning, ICML 2017
Y2 - 6 August 2017 through 11 August 2017
ER -