arXiv:2505.15354v2 Announce Type: replace 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 r
The proliferation of AI models across critical domains necessitates robust error correction, and this research addresses a core challenge: improving forecast accuracy without expensive retraining.
This development offers a practical, model-agnostic approach to enhance the reliability and application of AI-driven time series forecasts in sensitive sectors like finance and energy, reducing operational risk.
Traditional reliance on full model retraining to fix systemic errors can be reduced, favoring lightweight, adaptive optimization techniques that extend the utility of existing models and potentially accelerate deployment.
- · SaaS providers incorporating AI forecasts
- · Financial institutions
- · Energy grid operators
- · Healthcare providers
- · Developers solely focused on large-scale model retraining
- · Organizations with rigid model deployment pipelines
Improved accuracy of AI models for critical time series applications becomes more accessible.
Reduced computational cost and time associated with deploying and maintaining high-performance forecasting systems.
Increased adoption of AI in sectors previously hesitant due to concerns about forecast reliability and the cost of error correction.
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Read at arXiv cs.LG