| name | post-training-in-time-series-foundation-models-a-u |
| description | Skill generated from arXiv paper 2607.20002: Post-Training in Time Series Foundation Models: A Unifying Framework |
| metadata | {"arxiv":{"id":"2607.20002","title":"Post-Training in Time Series Foundation Models: A Unifying Framework","authors":["Shifeng Xie","Ambroise Odonnat","Zehao Xiao","Lei Zan","Malik Tiomoko","Lujia Pan","Themis Palpanas","Boris N. Oreshkin","Chenghao Liu","Keli Zhang"],"published":"2026-07-22","categories":["cs.LG","cs.AI"],"url":"https://arxiv.org/abs/2607.20002","utility":0.9}} |
Post-Training in Time Series Foundation Models: A Unifying Framework
arXiv: 2607.20002
Published: 2026-07-22
Authors: Shifeng Xie, Ambroise Odonnat, Zehao Xiao, Lei Zan, Malik Tiomoko, Lujia Pan, Themis Palpanas, Boris N. Oreshkin, Chenghao Liu, Keli Zhang
Categories: cs.LG, cs.AI
Utility: 0.90
Key Innovation
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 analyz...
Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.LG, cs.AI.
References
