用时间序列大模型做异常检测，零样本泛化能力强。
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
- 用大模型提取时序特征，再用BiLSTM+注意力细化模式
- 11个基准上平均AUC提升4.72%，AP提升6.60%
- 适合跨领域、小异常场景的工业/医疗/汽车系统
时序异常检测在金融、医疗和工业等领域至关重要。现有方法在不同数据集间泛化能力差，尤其对细微或上下文相关的异常表现不佳。为此，我们提出ChronosAD，一种基于时间序列基础模型的异常检测新架构。该方法采用两阶段流程：首先使用基础模型以零样本方式提取每个时序的嵌入；随后通过自研的时序块（由双向LSTM和多头注意力组成）进一步优化嵌入，捕捉时序依赖并突出显著模式。与以往方法不同，本模型只需极少任务特异性调优，在工业、医疗、网络物理及汽车系统等广泛领域均表现出强大泛化能力。在11个基准上的实验表明，ChronosAD在平均AUC上优于现有方法4.72%，在平均AP上提升6.60%。源码已公开于https://github.com/intelligolabs/ChronosAD。
原文摘要 · Abstract (English)
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor. Specifically, it employs a two-stage pipeline: first, it uses the foundation model to extract embeddings for each time series in a zero-shot manner. Then, a custom-developed Temporal Block, composed of Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Attention, refines these embeddings to capture temporal dependencies and highlight salient patterns. Unlike previous approaches, our model requires minimal task-specific tuning and demonstrates robust generalization across a wide range of domains, including industrial, medical, cyber-physical, and automotive systems. Extensive experiments on 11 benchmarks show that ChronosAD outperforms existing methods by 4.72% in AUC and 6.60% in AP on average. The source code is available at https://github.com/intelligolabs/ChronosAD.
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