{"ID":2887650,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01407","arxiv_id":"2508.01407","title":"Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.01407","url_pdf":"https://arxiv.org/pdf/2508.01407v3","authors":"[\"Hongwei Ma\",\"Junbin Gao\",\"Minh-Ngoc Tran\"]","published":"2025-08-02T15:25:10Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}