跨域时间序列异常检测新模型，兼顾效果与可解释性
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series
- 用专家混合框架提取通用异常特征，灵活且可解释
- 在多个异构源域上表现优于现有方法，跨域适应性强
- 适合工业场景中未知新领域的异常检测需求
无监督时间序列异常检测在各行业应用中至关重要。然而，由于不同领域间数据分布差异以及时间序列本身的非平稳性，现有方法在面对多源异构领域和新出现的未知目标领域时难以泛化。为此，我们提出CICADA（跨域可解释编码的异常检测与自适应模型），包含四项创新：(1) 使用专家混合（MOE）框架捕捉领域无关的异常特征，兼具灵活性与可解释性；(2) 提出新型选择性元学习机制，防止差异较大的领域间产生负向迁移；(3) 设计自适应扩展算法，支持新兴异构领域的动态扩展；(4) 采用分层注意力结构，在融合过程中量化各专家贡献，进一步提升可解释性。在合成数据及真实工业数据集上的大量实验表明，CICADA在跨域检测性能和可解释性方面均优于当前最先进方法。
原文摘要 · Abstract (English)
Unsupervised Time series anomaly detection plays a crucial role in applications across industries. However, existing methods face significant challenges due to data distributional shifts across different domains, which are exacerbated by the non-stationarity of time series over time. Existing models fail to generalize under multiple heterogeneous source domains and emerging unseen new target domains. To fill the research gap, we introduce CICADA (Cross-domain Interpretable Coding for Anomaly Detection and Adaptation), with four key innovations: (1) a mixture of experts (MOE) framework that captures domain-agnostic anomaly features with high flexibility and interpretability; (2) a novel selective meta-learning mechanism to prevent negative transfer between dissimilar domains, (3) an adaptive expansion algorithm for emerging heterogeneous domain expansion, and (4) a hierarchical attention structure that quantifies expert contributions during fusion to enhance interpretability further.Extensive experiments on synthetic and real-world industrial datasets demonstrate that CICADA outperforms state-of-the-art methods in both cross-domain detection performance and interpretability.
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