  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.
