arXiv: cs.AI preprintsInternational6 November 2026
Evaluating Language Model Features for Zero-Shot Time-Series Anomaly Detection Under Isolation Assumption
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arXiv:2611.07720v1 Announce Type: cross Abstract: Time-Series Anomaly Detection (TSAD) is a fundamentally challenging task: it is unsupervised, requires high local precision capabilities, and suffers from limited labelled data. Recent work has explored adapting Large Language Models (LLMs) to time-series via cross-modal transfer. However, existing approaches are poorly suited to Multivariate TSAD (MTSAD), as tokenisation often obscures fine-grained temporal and variables structures, and most methods optimise auxiliary objectives such as forecasting or reconstruction. We posit that these surrog
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