Add and Thin: Diffusion for Temporal Point Processes

David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, Stephan Günnemann

Keywords: Point Processes, Diffusion, Temporal Data, Generative Model, Forecasting, Density Estimation, Denoising

TL;DR: We connect diffusion models with TPPs by introducing a novel model that naturally models point processes.

Abstract: 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.

Submission Number: 6874