Add and Thin: Diffusion for Temporal Point Processes

• URL: http://arxiv.org/abs/2311.01139v2
• Date: Tue, 20 Feb 2024 07:39:16 GMT
• Title: Add and Thin: Diffusion for Temporal Point Processes
• Authors: David L\"udke, Marin Bilo\v{s}, Oleksandr Shchur, Marten Lienen, Stephan G\"unnemann
• Abstract summary: ADD-THIN is a principled probabilistic denoising diffusion model for temporal point process (TPP) networks. It operates on entire event sequences and matches state-of-the-art TPP models in density estimation. 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.
• Score: 24.4686728569167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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