🤖 AI Summary
This study evaluates whether foundation models can replace traditional supervised methods for real-world time series forecasting without task-specific training. It introduces a novel operational perspective by categorizing forecasting tasks into four representative scenarios and proposes a sequence-feature-based complexity-aware routing mechanism to automatically select the optimal model. Through extensive cross-domain benchmarking and zero-shot inference comparisons against supervised baselines, the work demonstrates that foundation models excel in settings with transferable periodic structures or cold-start conditions. The proposed routing strategy not only maintains competitive prediction accuracy but also significantly reduces inference overhead, outperforming uniform deployment of foundation models across all tasks.
📝 Abstract
Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve strong performance, they require domain-specific training, feature engineering, and ongoing maintenance. Large-scale foundation models have recently emerged as a zero-shot alternative, avoiding task-specific training much like LLMs. In this work, we evaluate foundation models against standard supervised approaches. Rather than focusing solely on aggregate accuracy, we analyze performance across four operational regimes: periodic human-centric systems, physically constrained processes, stochastic financial markets, and heterogeneous demand forecasting.
Our results characterize optimal deployment areas. Foundation models perform well in domains with transferable periodic structures and are efficient for cold-start or long-tail scenarios. Conversely, supervised specialists maintain higher precision in systems governed by strict physical constraints. In financial domains, newer foundation models are rapidly closing the performance gap with supervised specialists. We further quantify trade-offs in inference latency, data drift adaptability, and deployment constraints. Finally, we propose a Complexity Router that assigns each series to the optimal model class using empirical features. We demonstrate that this selective routing achieves higher accuracy and significantly lower inference costs compared to deploying a universal foundation model, providing a practical framework for balancing generalization and efficiency.