Marked Temporal Dynamics Modeling Based on Recurrent Neural Network

  • Yongqing Wang
  • Shenghua Liu
  • Huawei Shen
  • Jinhua Gao
  • Xueqi Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)


We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Experiments on two datasets demonstrate that the proposed method outperforms the state-of-the-art methods at predicting marked temporal dynamics.


Marked temporal dynamics Recurrent neural network Event stream data 



This work was funded by the National Basic Research Program of China (973 Program) under Grant Numbers 2013CB329602 and 2014CB340401, and the National Natural Science Foundation of China under Grant Numbers 61472400, 61572467, 61433014. H. W. Shen is also funded by Youth Innovation Promotion Association CAS and the CCF-Tencent RAGR (No. 20160107).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yongqing Wang
    • 1
  • Shenghua Liu
    • 1
  • Huawei Shen
    • 1
  • Jinhua Gao
    • 1
  • Xueqi Cheng
    • 1
  1. 1.CAS Key Laboratory of Network Data Science and Technology, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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