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.
翻译:在本文中,我们解决了对事件序列进行长方位预测这一重要但调查不足的问题。现有的先进模型由于其自动递减结构而不能很好地完成这一任务。我们提出了HYPRO,这是一个自然适合这项任务的混合、正常化的概率模型:其第一部分是一个自动递减基模型,它学会提出预测;其第二部分是一个能源功能,它学会重新权衡建议,以便更现实的预测最终产生更高的概率。我们还为这一模型提出了有效的培训和推断算法。对多个现实世界数据集的实验表明,我们提议的HYPRO模型在对未来事件作出长方位预测时可以大大优于以前的模型。我们还进行了一系列关联研究,以调查我们拟议方法的每个组成部分的有效性。