arXiv Machine Learning July 15, 2026 By Martin Uray, Saverio Messineo, Roland Kwitt, Stefan Huber

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.

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Related stories

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