Intensity-free Convolutional Temporal Point Process: Incorporating Local
  and Global Event Contexts
Wang-Tao Zhou Zhao Kang Ling Tian Yi Su
Event prediction in the continuous-time domain is a crucial but rather
difficult task. Temporal point process (TPP) learning models have shown great
advantages in this area. Existing models mainly focus on encoding global
contexts of events using techniques like recurrent neural networks (RNNs) or
self-attention mechanisms. However, local event contexts also play an important
role in the occurrences of events, which has been largely ignored. Popular
convolutional neural networks, which are designated for local context
capturing, have never been applied to TPP modelling due to their incapability
of modelling in continuous time. In this work, we propose a novel TPP modelling
approach that combines local and global contexts by integrating a
continuous-time convolutional event encoder with an RNN. The presented
framework is flexible and scalable to handle large datasets with long sequences
and complex latent patterns. The experimental result shows that the proposed
model improves the performance of probabilistic sequential modelling and the
accuracy of event prediction. To our best knowledge, this is the first work
that applies convolutional neural networks to TPP modelling.