FOCA: Foundation-model-based One-Class Anomaly Detection for Time Series

Time series anomaly detection is challenging due to the rarity of anomalies and the complexity and diversity of normal patterns. Most existing methods rely on a single hypothesis and learn feature patterns from a limited dataset, which restricts their generalization capabilities. At the same time, time series foundation models have shown promising results across multiple tasks due to their strong generalization capabilities. However, time series foundation models are less likely to achieve better performance in complex anomaly detection tasks. To address this issue, this paper introduces FOCA, a novel foundation-model-based one-class anomaly detection approach. This method preserves the generalization ability of the foundation model to capture normal variation patterns and provides a comprehensive feature space for one-class classification. It constrains normal features within a sufficiently small hypersphere to construct a decision boundary for detecting abnormal data. Furthermore, it is observed in practice that the introduction of fine-tuning techniques can further improve the performance of the method. Extensive experiments on two standard benchmark datasets demonstrate that our method outperforms the state-of-the-art approaches.