Benchmarking Foundation Models for Times-Series Forecasting: Zero-Shot, Few-Shot, and Full-Shot Evaluations

Recently, time-series forecasting foundation models trained on large, diverse datasets have demonstrated robust zero-shot and few-shot capabilities. Given the ubiquity of time-series data in IoT, finance, and industrial applications, rigorous benchmarking is essential to assess their forecasting performance and overall value. In this study, our objective is to benchmark foundational models from Amazon, Salesforce, and Google against traditional statistical and deep learning baselines on both public and proprietary industrial datasets. We evaluate zero-shot, few-shot, and full-shot scenarios using metrics such as sMAPE and NMAE on fine-tuned models, ensuring reliable comparisons. All experiments are conducted with onTime, our dedicated open-source library that guarantees reproducibility, data privacy, and flexible configuration. Our results show that foundation models often outperform traditional methods with minimal dataset-specific tuning, underscoring their potential to simplify forecasting tasks and bridge performance gaps in data-scarce settings. Additionally, we address non-performance criteria—such as integration ease, model size, and inference/training time, which are critical for real-world deployment.