Integrate lightweight hybrid techniques

Background: Forecasting models are increasingly relying on complex deep learning architectures that demand significant computational resources and may lack transparency for operational users.

Question / Future Work: Extend the framework to incorporate lightweight hybrid techniques that combine interpretable additive models (like Prophet) with targeted correction or decomposition modules to specifically improve robustness when the pollution regimes change.

Why It Matters: This direction seeks to bridge the gap between high-accuracy complex models and the operational need for interpretable, low-overhead, and robust solutions for unpredictable pollution shifts.

Evidence: One promising direction is the integration of lightweight hybrid techniques that combine interpretable additive models with targeted correction or decomposition modules to improve robustness under changing pollution regimes.