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.
@article{arxiv.2311.01139,
title = {Add and Thin: Diffusion for Temporal Point Processes},
author = {David Lüdke and Marin Biloš and Oleksandr Shchur and Marten Lienen and Stephan Günnemann},
journal= {arXiv preprint arXiv:2311.01139},
year = {2024}
}