Title: Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
Primary area: interpretability and explainable AI
Keywords: Cross-domain generalization, Differentiable physics residuals, Physics-regularized forecasting, Time-series Transformer

Abstract: Accurate, explainable and physically-credible forecasting remains a persistent challenge for multivariate time-series with domain-varying statistical properties. 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-principle constraints. Unlike prior efficiency-focused Transformers, such as Informer, which leverages sparsity, or FEDformer, which incorporates frequency priors, DORIC combines latent explainability with explicit scientific constraints, all while preserving the attention mechanism’s capacity for modeling long-range dependencies.  We evaluate DORIC on six publicly-available datasets: sub-hourly Electricity and Traffic, hourly ETT, high-dimensional Weather, weekly Influenza-like Illness, and minute-level Exchange Rate. DORIC achieves the lowest error in eight of twelve MSE/MAE metrics.  Compared with TimeMixer, DORIC outperforms it on 4 datasets, while maintaining excellent explainability. Ablation studies reveal complementary contributions: removing the physics residual increases average MSE from 0.328 to 0.547, eliminating concept alignment pushes it to 0.698, and replacing the shared encoder with disjoint concept heads results in a  76% increase.