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This paper introduces a unifying framework for post-training methods in time series foundation models (TSFMs), addressing the challenges of domain shift, task heterogeneity, and limited supervision that hinder reliable deployment. By categorizing post-training interventions into five distinct areas鈥攑arameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization鈥攖he authors analyze existing methods and their limitations. The findings highlight the need for future research directions focused on controlled adaptation and uncertainty-aware model composition to enhance the reliability of TSFMs in practical applications.
Bridging the gap between pretrained time series models and reliable deployment could redefine how we approach time series analysis in diverse applications.
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, which motivates post-training as a broad class of methods to adapt, augment, compose, calibrate, or specialize pretrained TSFMs for downstream tasks. In this work, we analyze TSFM post-training methods based on their locus of intervention in the prediction pipeline, yielding five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization. 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. Overall, 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.
