Continuous-time long-term event prediction plays an important role in many application scenarios. Most existing works rely on autoregressive frameworks to predict event sequences, which suffer from error accumulation, thus compromising prediction quality. Inspired by the success of denoising diffusion probabilistic models, we propose a diffusion-based non-autoregressive temporal point process model for long-term event prediction in continuous time. Instead of generating events one at a time in an autoregressive way, our model predicts the future event sequence entirely as a whole. In order to perform diffusion processes on event sequences, we develop a bidirectional map between target event sequences and the Euclidean vector space. Furthermore, we design a novel denoising network to capture both sequential and contextual features for better sample quality. Extensive experiments are conducted to prove the superiority of our proposed model over state-of-the-art methods on long-term event prediction in continuous time. To the best of our knowledge, this is the first work to apply diffusion methods to long-term event prediction problems.

翻译：连续时间长期事件预测在许多应用场景中扮演着重要角色。现有工作大多依赖自回归框架预测事件序列，这会导致误差累积，从而降低预测质量。受去噪扩散概率模型成功应用的启发，我们提出了一种基于扩散的非自回归时间点过程模型，用于连续时间下的长期事件预测。不同于自回归方式逐个生成事件，我们的模型将未来事件序列作为一个整体进行完整预测。为了实现事件序列上的扩散过程，我们开发了目标事件序列与欧几里得向量空间之间的双向映射。此外，我们设计了一种新颖的去噪网络，以捕获序列特征和上下文特征，从而提升样本质量。通过大量实验，我们证明了所提模型在连续时间长期事件预测任务上优于现有最优方法。据我们所知，这是首次将扩散方法应用于长期事件预测问题的研究工作。