Post-Training in Time Series Foundation Models: A Unifying Framework
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 process...
| Authors | Shifeng Xie, Ambroise Odonnat, Zehao Xiao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 15 September 2026 |
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