CODA: Time-Series Forecasting through Auditable Concept-State Transitions

Accurate long-horizon forecasting requires explanations tied to the prediction mechanism. We propose CODA, a Consistency-Driven Concept-State Architecture that learns five softly anchored temporal concepts and predicts through a direct, horizon-specific affine transition. The history encoder remains expressive, while the final head exposes the exact contribution of each concept and the last observation. Analytic targets stabilize concept meaning, and local compatibility penalties regularize the explanatory state. Across six public benchmarks, CODA achieves the lowest error in 10 of 12 primary-horizon MSE/MAE comparisons among the evaluated methods. In a separate multi-horizon evaluation against N-HiTS and ModernTCN, it attains the lowest MSE in 19 of 24 dataset–horizon settings and ties in one more. Concept-target $R^2$ ranges from 0.62 to 0.94, and removing semantic anchoring increases average MSE from 0.268 to 0.741. Controlled concept and residual ablations, an explicit Electricity coefficient analysis, and input-noise tests connect predictive performance with inspectable model behavior. CODA thus offers accurate forecasting through a compact semantic interface whose downstream effects are analytically verifiable.