Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis

Javier Pulido, Filipe Rodrigues|February 27, 2026arXiv

Key Takeaway

Foundation models trained on diverse time-series data can forecast transportation metrics without task-specific tuning, making them practical basel...

Summary

This paper tests whether a general-purpose time-series AI model (Chronos-2) can forecast transportation data like traffic volume and bike-sharing demand without any custom training. The model works surprisingly well out-of-the-box, often beating specialized models built just for these tasks, and also provides useful uncertainty estimates.

evaluation applications efficiency

Key Terms

foundation-models zero-shot-learning time-series-forecasting uncertainty-quantification