arXiv:2605.26161v1 Announce Type: new 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. 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
The proliferation and increasing scale of Time Series Foundation Models necessitate robust auditing mechanisms for data contamination, especially as these models move towards wider deployment.
Contaminated training data can lead to misleading performance metrics and undermine trust in large-scale AI models, impacting investment and adoption, particularly in critical applications.
The explicit formalization and study of pretraining contamination auditing for TSFMs introduce new methodologies and standards for model development and evaluation in a growing field.
- · AI auditing firms
- · Responsible AI developers
- · Data governance specialists
- · Academic researchers in AI ethics
- · Developers of proprietary models lacking transparency
- · Organizations relying on unverified model performance claims
- · Competitors using contaminated benchmarks
Increased focus on data provenance and documentation for large-scale AI model training datasets.
Development of industry standards and regulatory requirements for contamination auditing in foundation models across various domains.
A potential shift towards decentralized or federated learning approaches to mitigate large-scale data contamination risks in foundation models.
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Read at arXiv cs.LG