Add and Thin: Diffusion for Temporal Point Processes
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866929248482099200 |
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| author | Lüdke, David Biloš, Marin Shchur, Oleksandr Lienen, Marten Günnemann, Stephan |
| author_facet | Lüdke, David Biloš, Marin Shchur, Oleksandr Lienen, Marten Günnemann, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01139 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Add and Thin: Diffusion for Temporal Point Processes Lüdke, David Biloš, Marin Shchur, Oleksandr Lienen, Marten Günnemann, Stephan Machine Learning 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. |
| title | Add and Thin: Diffusion for Temporal Point Processes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.01139 |