Automatic selection of the best neural architecture for time series forecasting

This is primarily an academic research project/paper with very low community traction (0 stars). While it addresses a valid problem (architecture selection for time series), it competes with the broader trend of 'foundation models' for time series (like Google's TimesFM or Amazon's Chronos) which aim to provide zero-shot forecasting, potentially making dataset-specific architecture search less critical.