用潜在空间生成假异常，让无监督时序检测更准
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
- 在潜在空间直接生成假异常，无需人工设计异常模式
- 在三个基准数据集上达到当前最优性能
- 适合需要低依赖标注的工业与医疗时序监控场景
时序异常检测在工业监控、医疗和网络安全等领域至关重要，但因异常稀少且类型多样，加之标注数据稀缺，仍具挑战。无监督方法虽为主流，但现有方法多依赖重构或预测，难以处理复杂数据；或基于嵌入的方法需领域特定异常合成与固定距离度量。本文提出ASTER框架，直接在潜在空间生成伪异常，避免人工异常注入与领域知识依赖。通过潜在空间解码器生成适配的伪异常以训练基于Transformer的异常分类器，并利用预训练大模型增强该空间的时间与上下文表征。在三个基准数据集上的实验表明，ASTER达到当前最优性能，为基于大模型的时序异常检测树立新标准。
原文摘要 · Abstract (English)
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.
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