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.

我们提出了一种名为HYPRO的混合归一化概率模型，通过学习自编码和能量函数来进行长时间序列预测，并通过多种现实数据集的实验表明，在对未来事件进行长时间预测方面，我们的模型具有显著优势。

HYPRO：一种混合归一化概率模型用于事件序列的长期预测