Title: Enhancing Time Series Forecasting Through Human-in-the-Loop Adaptive Optimization

Time series prediction models frequently exhibit systematic, foreseeable inaccuracies, a challenge that persists even in vital sectors like healthcare, finance, and energy. To address this, we present a novel post-training adaptive optimization framework designed to boost forecast precision without necessitating architectural modifications or retraining. This approach automatically implements expressive transformations, optimized through methods such as reinforcement learning, genetic algorithms, or contextual bandits, to rectify model outputs in a manner that is both lightweight and model-agnostic.

From a theoretical standpoint, we demonstrate that affine corrections consistently decrease mean squared error. In practice, this concept is expanded through dynamic action-based optimization. The system also incorporates an optional human-in-the-loop feature, allowing domain specialists to direct corrections via natural language inputs, which are then interpreted and converted into actions by a language model.

Testing across various benchmarks, including electricity usage, weather patterns, and traffic data, reveals consistent improvements in accuracy with negligible computational cost. Furthermore, our interactive demonstration highlights the framework’s capability for real-time application. By merging automated post-hoc refinement with mechanisms that are both interpretable and extensible, this strategy provides a robust new pathway for practical forecasting systems.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC