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[2510.13654] Challenges and Requirements for Benchmarking Time Series Foundation Models
Summary
This article discusses the challenges and requirements for benchmarking Time Series Foundation Models (TSFMs), highlighting issues of information leakage that can lead to misleading performance estimates.
Why It Matters
As TSFMs emerge as a new paradigm in time-series forecasting, understanding their evaluation is crucial for ensuring their reliability in real-world applications. The article emphasizes the need for robust methodologies to prevent information leakage, which is vital for the integrity of machine learning models.
Key Takeaways
- TSFMs promise zero-shot predictions but face evaluation challenges.
- Information leakage can occur through dataset overlaps and temporal correlations.
- Robust evaluation methodologies are needed to ensure accurate performance estimates.
- The research community is urged to adopt principled approaches for TSFM evaluation.
- Understanding these challenges is essential for advancing time-series forecasting.
Computer Science > Machine Learning arXiv:2510.13654 (cs) [Submitted on 15 Oct 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Challenges and Requirements for Benchmarking Time Series Foundation Models Authors:Marcel Meyer, Sascha Kaltenpoth, Kevin Zalipski, Oliver Müller View a PDF of the paper titled Challenges and Requirements for Benchmarking Time Series Foundation Models, by Marcel Meyer and 3 other authors View PDF HTML (experimental) Abstract:Time Series Foundation Models (TSFMs) represent a new paradigm for time-series forecasting, promising zero-shot predictions without the need for task-specific training or fine-tuning. However, similar to Large Language Models (LLMs), the evaluation of TSFMs is challenging: as training corpora grow increasingly large, it becomes difficult to ensure the integrity of the test sets used for benchmarking. Our investigation of existing TSFM evaluation studies identifies two kinds of information leakage: (1) train-test sample overlaps arising from the multi-purpose reuse of datasets and (2) temporal overlap of correlated train and test series. Ignoring these forms of information leakage when benchmarking TSFMs risks producing overly optimistic performance estimates that fail to generalize to real-world settings. We therefore argue for the development of novel evaluation methodologies that avoid pitfalls already observed in both LLM and classical time-series benchmarking, and we call on the research community to adopt pri...
