Diffusion-based Time Series Forecasting for Sewerage Systems
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909645148258304 |
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| author | Pearson, Nicholas A. Cairoli, Francesca Bortolussi, Luca Russo, Davide Zanello, Francesca |
| author_facet | Pearson, Nicholas A. Cairoli, Francesca Bortolussi, Luca Russo, Davide Zanello, Francesca |
| contents | We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By developing a diffusion-based model that processes multivariate time series data, our system excels at capturing complex correlations across diverse environmental signals, enabling robust predictions even during extreme weather events. To strengthen the model's reliability, we further calibrate its predictions with a conformal inference technique, tailored for probabilistic time series data, ensuring that the resulting prediction intervals are statistically reliable and cover the true target values with a desired confidence level. Our empirical tests on real sewerage system data confirm the model's exceptional capability to deliver reliable contextual predictions, maintaining accuracy even under severe weather conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-based Time Series Forecasting for Sewerage Systems Pearson, Nicholas A. Cairoli, Francesca Bortolussi, Luca Russo, Davide Zanello, Francesca Machine Learning Artificial Intelligence We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By developing a diffusion-based model that processes multivariate time series data, our system excels at capturing complex correlations across diverse environmental signals, enabling robust predictions even during extreme weather events. To strengthen the model's reliability, we further calibrate its predictions with a conformal inference technique, tailored for probabilistic time series data, ensuring that the resulting prediction intervals are statistically reliable and cover the true target values with a desired confidence level. Our empirical tests on real sewerage system data confirm the model's exceptional capability to deliver reliable contextual predictions, maintaining accuracy even under severe weather conditions. |
| title | Diffusion-based Time Series Forecasting for Sewerage Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.08577 |