ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processes

This work introduces a diffusion model-based approach to modelling temporal point processes via an asynchronous noise schedule. Existing methods typically rely on parametric conditional intensity functions or autoregressive next-event prediction, which can limit distributional expressivity and make long-horizon forecasting computationally expensive. We address this limitation by using diffusion models to learn the joint distribution of event sequences in latent space without imposing restrictive parametric assumptions. At each step of the diffusion process, the noise schedule injects noise of varying scales into different parts of the data. With a careful design of the noise schedules, earlier events are generated faster than later ones, thus providing stronger conditioning for forecasting the more distant future. We derive an objective to effectively train these models for a general family of noise schedules based on conditional flow matching. Our method models the joint distribution of the latent representations of events in a sequence and achieves state-of-the-art results in predicting both the next inter-event time and event type on benchmark datasets. Additionally, it flexibly accommodates varying lengths of observation and prediction windows in different forecasting settings by adjusting the starting and ending points of the generation process. Finally, our method shows strong performance in long horizon prediction tasks, outperforming existing baseline methods.

Authors

• Amartya Mukherjee
• Ruizhi Deng
• He Zhao University of Liverpool
• Yuzhen Mao
• Leonid Sigal
• Frederick Tung

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