HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

Siqiao Xue, Xiaoming Shi, James Y. Zhang, Hongyuan Mei

Keywords: probabilistic model, event sequences, energy-based model, long-horizon prediction

Abstract: In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure. We propose HYPRO, a hybridly normalized probabilistic model that naturally fits this task: its first part is an autoregressive base model that learns to propose predictions; its second part is an energy function that learns to reweight the proposals such that more realistic predictions end up with higher probabilities. We also propose efficient training and inference algorithms for this model. Experiments on multiple real-world datasets demonstrate that our proposed HYPRO model can significantly outperform previous models at making long-horizon predictions of future events. We also conduct a range of ablation studies to investigate the effectiveness of each component of our proposed methods.

TL;DR: We develop a hybridly normalized model, along with efficient training and inference algorithms, for the task of long-horizon prediction of event sequences.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2210.01753/code)