Hawkes Process with Stochastic Triggering Kernel

摘要：

　　The impact from past to future is a vital feature in modelling time series data,which has been described by many point processes,e.g.the Hawkes process.In classical Hawkes process,the triggering kernel is assumed to be a deterministic function.However,the triggering kernel can vary with time due to the system uncertainty in real applications.To model this kind of variance,we propose a Hawkes process variant with stochastic triggering kernel,which incorporates the variation of triggering kernel over time.In this model,the triggering kernel is considered to be an independent multivariate Gaussian distribution.We derive and implement a tractable inference algorithm based on variational autoencoder.Results from synthetic and real data experiments show that the underlying mean triggering kernel and variance band can be recovered,and using the stochastic triggering kernel is more accurate than the vanilla Hawkes process in capacity planning.

关键词： Hawkes process Stochastic triggering kernel

作者: Feng Zhou Yixuan Zhang Zhidong Li Xuhui Fan Yang Wang Arcot Sowmya Fang Chen

作者单位: University of New South Wales,Sydney,Australia;CSIRO DATA61,Sydney,Australia The University of Sydney,Sydney,Australia University of Technology Sydney,Sydney,Australia;CSIRO DATA61,Sydney,Australia University of New South Wales,Sydney,Australia

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 319-330

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）