Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts

TL;DR

This paper introduces a novel intensity-free convolutional temporal point process model that combines local and global event contexts, improving event prediction accuracy and scalability in continuous-time event modeling.

Contribution

It is the first to apply convolutional neural networks to TPP modeling, integrating local and global contexts with an RNN for enhanced performance.

Findings

Improved probabilistic sequential modeling accuracy

Enhanced event prediction performance

Scalable to large, complex datasets

Abstract

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.