Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed during pretraining and thus yield overly optimistic performance estimates. Auditing such contamination is challenging in time series because signals are continuous and heterogeneous, and often lack corpus documentation. To the best of our knowledge, this is the first work to study pretraining contamination auditing for TSFMs. We formalize the problem of pretraining contamination auditing for TSFMs and propose TSFMAudit, a method based on probe adaptation dynamics. Our key intuition is that contamination manifests as unusually efficient adaptation: after a fine tuning probe, contaminated datasets tend to exhibit faster loss reduction with smaller backbone movement. We evaluate TSFMAudit on 6 TSFMs and 187 datasets using documented training source evidence as supervision, and compare against 10 competitive baselines adapted from the LLM literature.
Cite
@article{arxiv.2605.26161,
title = {TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models},
author = {Hongkai Li and Shifeng Xie and Lefei Shen and Zhuo Li and Mouxiang Chen and Xiaobin Zhang and Han Fu and Jianling Sun and Xiaoxue Ren and Chenghao Liu},
journal= {arXiv preprint arXiv:2605.26161},
year = {2026}
}