Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expressively capture event sequences in a one-step-ahead fashion, they are inherently limited for long-term forecasting applications due to the accumulation of errors caused by their sequential nature. To overcome these limitations, we derive ADD-THIN, a principled probabilistic denoising diffusion model for TPPs that operates on entire event sequences. Unlike existing diffusion approaches, ADD-THIN naturally handles data with discrete and continuous components. In experiments on synthetic and real-world datasets, our model matches the state-of-the-art TPP models in density estimation and strongly outperforms them in forecasting.

翻译：在时间点过程（TPP）框架下的自回归神经网络已成为建模连续时间事件数据的标准方法。尽管这些模型能够以一步预测的方式生动地捕捉事件序列，但由于其序列特性导致的误差累积，它们在长期预测应用中存在固有限制。为克服这些局限，我们推导出ADD-THIN——一种基于概率去噪扩散模型的原生TPP框架，可对完整事件序列进行操作。与现有扩散方法不同，ADD-THIN天然适用于包含离散与连续成分的数据。在合成数据集与现实世界数据集上的实验中，我们的模型在密度估计方面达到了最先进的TPP模型水平，且在预测任务中显著优于它们。