Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data. This scarcity makes unsupervised approaches predominant, yet existing methods often rely on reconstruction or forecasting, which struggle with complex data, or on embedding-based approaches that require domain-specific anomaly synthesis and fixed distance metrics. We propose ASTER, a framework that generates pseudo-anomalies directly in the latent space, avoiding handcrafted anomaly injections and the need for domain expertise. A latent-space decoder produces tailored pseudo-anomalies to train a Transformer-based anomaly classifier, while a pre-trained LLM enriches the temporal and contextual representations of this space. Experiments on three benchmark datasets show that ASTER achieves state-of-the-art performance and sets a new standard for LLM-based TSAD.

翻译：时间序列异常检测在工业监控、医疗健康和网络安全等领域至关重要，但因异常事件罕见且异质性高、标注数据稀缺而面临挑战。数据稀缺使得无监督方法成为主流，然而现有方法通常依赖重构或预测（难以处理复杂数据），或基于嵌入的方法（需要领域特定的异常合成和固定距离度量）。本文提出ASTER框架，直接在潜在空间中生成伪异常，无需人工注入异常或领域知识。该框架通过潜在空间解码器生成定制化伪异常，用于训练基于Transformer的异常分类器，并利用预训练大语言模型增强该空间的时序与上下文表征。在三个基准数据集上的实验表明，ASTER取得了最先进性能，为基于大语言模型的时间序列异常检测树立了新标杆。