Abstract
Time Series Foundation Models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further intervention to handle domain shift, task heterogeneity, limited supervision, and computational constraints, motivating post-training as a broad class of methods to adapt, augment, compose, calibrate, or specialize pretrained TSFMs for downstream tasks.
Analysis of Post-Training Methods
We categorize TSFM post-training methods based on their locus of intervention in the prediction pipeline into five categories:
- Parameter Adaptation: Adjusting model parameters to fit specific tasks.
- Context Augmentation: Improving the input context of the model by introducing additional information.
- Model Composition: Combining multiple models to enhance overall performance.
- Output Processing and Uncertainty Control: Optimizing model outputs and managing prediction uncertainty.
- Compression and Specialization: Reducing model complexity and optimizing for specific applications.
Within each category, we study main representative methods and discuss their current limitations.
We further identify future directions toward controlled adaptation, reliable context construction, uncertainty-aware model composition, calibrated output processing, and deployment-aware specialization.
Conclusion
By providing a unifying framework for the emerging TSFM post-training landscape, this work aims to support future research to navigate the design space between a pretrained TSFM and its reliable downstream deployment.
Blogger's Review: This paper offers a comprehensive classification and analysis of post-training methods for time series foundation models, providing a clear framework that helps researchers understand how to effectively apply pretrained models to practical tasks. Especially when facing diverse tasks and data, post-training strategies become crucial, and future research can delve deeper into this theme.