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In this paper, we tackle the important yet under-investigated problem ofmaking long-horizon prediction of event sequences. Existing state-of-the-artmodels do not perform well at this task due to their autoregressive structure.We propose HYPRO, a hybridly normalized probabilistic model that naturally fitsthis task: its first part is an autoregressive base model that learns topropose predictions; its second part is an energy function that learns toreweight the proposals such that more realistic predictions end up with higherprobabilities. We also propose efficient training and inference algorithms forthis model. Experiments on multiple real-world datasets demonstrate that ourproposed HYPRO model can significantly outperform previous models at makinglong-horizon predictions of future events. We also conduct a range of ablationstudies to investigate the effectiveness of each component of our proposedmethods.

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