TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
The paper titled 'TSFMAudit' addresses the issue of data contamination in time series foundation models (TSFMs). It introduces a method for auditing pretraining contamination, which can lead to overly optimistic performance estimates. The authors evaluate their approach on multiple datasets and compare it against existing baselines.
- ▪Time series foundation models are pretrained on large datasets, raising concerns about contamination in evaluation datasets.
- ▪The proposed method, TSFMAudit, is based on probe adaptation dynamics to identify contamination.
- ▪The study evaluates TSFMAudit on 6 TSFMs and 187 datasets, using documented training source evidence for supervision.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26161 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
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| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.26161 (cs) [Submitted on 24 May 2026] Title:TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models Authors:Hongkai Li, Shifeng Xie, Lefei Shen, Zhuo Li, Mouxiang Chen, Xiaobin Zhang, Han Fu, Jianling Sun, Xiaoxue Ren, Chenghao Liu View a PDF of the paper titled TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models, by Hongkai Li and 9 other authors View PDF HTML (experimental) Abstract: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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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