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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01407 |
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| _version_ | 1866912739036758016 |
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| author | Ma, Hongwei Gao, Junbin Tran, Minh-Ngoc |
| author_facet | Ma, Hongwei Gao, Junbin Tran, Minh-Ngoc |
| contents | Accurate, explainable and physically credible forecasting remains a persistent challenge for multivariate time-series whose statistical properties vary across domains. We propose DORIC, a Domain-Universal, ODE-Regularized, Interpretable-Concept Transformer for Time-Series Forecasting that generates predictions through five self-supervised, domain-agnostic concepts while enforcing differentiable residuals grounded in first-principles constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting Ma, Hongwei Gao, Junbin Tran, Minh-Ngoc Machine Learning Accurate, explainable and physically credible forecasting remains a persistent challenge for multivariate time-series whose statistical properties vary across domains. We propose DORIC, a Domain-Universal, ODE-Regularized, Interpretable-Concept Transformer for Time-Series Forecasting that generates predictions through five self-supervised, domain-agnostic concepts while enforcing differentiable residuals grounded in first-principles constraints. |
| title | Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.01407 |