From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917115233042432 |
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| author | Ma, Hongwei Gao, Junbin Tran, Minh-ngoc |
| author_facet | Ma, Hongwei Gao, Junbin Tran, Minh-ngoc |
| contents | Long-horizon multivariate time-series forecasting is challenging because realistic predictions must (i) denoise heterogeneous signals, (ii) track time-varying cross-series dependencies, and (iii) remain stable and physically plausible over long rollout horizons. We present PRISM, which couples a score-based diffusion preconditioner with a dynamic, correlation-thresholded graph encoder and a forecast head regularized by generic physics penalties. We prove contraction of the induced horizon dynamics under mild conditions and derive Lipschitz bounds for graph blocks, explaining the model's robustness. On six standard benchmarks , PRISM achieves consistent SOTA with strong MSE and MAE gains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs Ma, Hongwei Gao, Junbin Tran, Minh-ngoc Machine Learning Long-horizon multivariate time-series forecasting is challenging because realistic predictions must (i) denoise heterogeneous signals, (ii) track time-varying cross-series dependencies, and (iii) remain stable and physically plausible over long rollout horizons. We present PRISM, which couples a score-based diffusion preconditioner with a dynamic, correlation-thresholded graph encoder and a forecast head regularized by generic physics penalties. We prove contraction of the induced horizon dynamics under mild conditions and derive Lipschitz bounds for graph blocks, explaining the model's robustness. On six standard benchmarks , PRISM achieves consistent SOTA with strong MSE and MAE gains. |
| title | From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.17817 |