Structured Noise Modeling for Enhanced Time-Series Forecasting

Improved reliability in critical infrastructure decisions: The framework demonstrates consistent accuracy gains across real-world datasets (electricity, traffic, and solar), which is significant because forecasting errors in these domains directly impact infrastructure management and resource allocation affecting public services.

Transparency through modular design: The "blur-denoise" approach creates interpretable separation between broad trend capture and fine-detail refinement, making it easier for domain experts and stakeholders to understand how the model reaches its predictions—a key requirement for trustworthy AI in high-stakes applications.

Practical adaptability for resource-constrained settings: The lightweight enhancement layer can be applied to existing pretrained models, enabling organizations with limited data or computational resources to improve forecast reliability without rebuilding systems from scratch, democratizing access to more stable predictions.

Reduced prediction instability: By using structured noise modeling rather than random corruption, the framework produces more stable predictions across multiple time horizons, addressing a core reliability concern that undermines confidence in AI-driven decision support systems.

Responsible scaling of AI capabilities: The work explicitly connects technical improvements to "trustworthy AI systems" in time-critical domains, acknowledging that better forecasting accuracy alone is insufficient—the framework prioritizes interpretability and stability alongside performance, supporting more responsible deployment in energy and infrastructure sectors.