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.

在连续时间事件数据的时间点过程（TPP）框架中，自回归神经网络已成为建模连续时间事件数据的标准。为了克服这些限制，我们推导出ADD-THIN，一种基于整个事件序列的原则性概率去噪扩散模型，它与现有的扩散方法不同，可以自然地处理具有离散和连续分量的数据。在合成和真实数据集的实验中，我们的模型在密度估计方面与最先进的TPP模型相匹配，并在预测方面明显优于它们。

增加与稀疏：用于时间点过程的扩散