Attentive Neural Point Processes for Event Forecasting

Authors

  • Yulong Gu Alibaba Group

Keywords:

Time-Series/Data Streams, Web Personalization & User Modeling, Mining of Spatial, Temporal or Spatio-Temporal Da, Recommender Systems & Collaborative Filtering

Abstract

Event sequence, where each event is associated with a marker and a timestamp, is increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to be a crucial problem, which aims to predict the next event based on the historical sequence. In this paper, we propose ANPP, an Attentive Neural Point Processes framework to solve this problem. In comparison with state-of-the-art methods like recurrent marked temporal point processes, ANPP leverages the time-aware self-attention mechanism to explicitly model the influence between every pair of historical events, resulting in more accurate predictions of events and better interpretation ability. Extensive experiments on one synthetic and four real-world datasets demonstrate that ANPP can achieve significant performance gains against state-of-the-art methods for predictions of both timings and markers. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/AAAI2021\_ANPP.

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Published

2021-05-18

How to Cite

Gu, Y. (2021). Attentive Neural Point Processes for Event Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7592-7600. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16929

Issue

Section

AAAI Technical Track on Machine Learning II