Human in the Loop Adaptive Optimization for Improved Time Series Forecasting

2025-05-21

AI Summary

Temporal forecasting models exhibit systematic errors in critical domains such as energy, finance, and healthcare. Method: This paper proposes a lightweight, post-hoc adaptive optimization framework that requires no model retraining or architectural modification. It unifies reinforcement learning, contextual bandits, and genetic algorithms for optimization over dynamic action spaces; theoretically proves that affine correction strictly reduces mean squared error (MSE); and introduces a novel natural language–driven human-in-the-loop correction mechanism, wherein large language models parse NL instructions into executable correction actions. Contribution/Results: Evaluated across multi-source benchmark datasets—including power systems, meteorology, and transportation—the framework achieves significant accuracy improvements with negligible computational overhead, supports real-time interactive correction, and bridges theoretical rigor with practical deployability.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Time series forecasting models often produce systematic, predictable errors even in critical domains such as energy, finance, and healthcare. We introduce a novel post training adaptive optimization framework that improves forecast accuracy without retraining or architectural changes. Our method automatically applies expressive transformations optimized via reinforcement learning, contextual bandits, or genetic algorithms to correct model outputs in a lightweight and model agnostic way. Theoretically, we prove that affine corrections always reduce the mean squared error; practically, we extend this idea with dynamic action based optimization. The framework also supports an optional human in the loop component: domain experts can guide corrections using natural language, which is parsed into actions by a language model. Across multiple benchmarks (e.g., electricity, weather, traffic), we observe consistent accuracy gains with minimal computational overhead. Our interactive demo shows the framework's real time usability. By combining automated post hoc refinement with interpretable and extensible mechanisms, our approach offers a powerful new direction for practical forecasting systems.

Problem

• Reduces systematic errors in time series forecasting models
• Improves forecast accuracy without retraining or architectural changes
• Supports human-guided corrections via natural language input

Innovation

• Post training adaptive optimization framework
• Dynamic action based optimization methods
• Human in the loop via natural language