时间序列异常检测的上下文锚定配对监督恢复
Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing
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- Tsinghua University（清华大学）
- Huawei（华为）
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中文总结 AI 辅助
提出CAPS框架，通过上下文锚定配对监督恢复，解决时间序列异常检测中标签稀缺和上下文依赖问题，在九个数据集上取得最优综合性能。
中文摘要 AI 辅助
时间序列异常检测（TSAD）仍然具有挑战性，不仅因为异常标签稀缺，还因为时间异常高度依赖于上下文。现有方法通常依赖无监督目标或替代异常模式，为上下文相关的正常-异常区分提供的监督有限。我们提出上下文锚定配对监督（CAPS），一种用于TSAD的监督恢复框架。CAPS将理想异常监督视为在相同时间上下文下正常与异常结果之间的匹配比较，并寻求在没有目标域异常标签的情况下恢复此类监督。通过模拟正常-异常配对，CAPS通过重构、背景一致性和配对内反事实重组学习结构和异常语义表示。所得的异常表示形成一个具有粗略模式的连续语义空间，并诱导可采样的多模态先验。CAPS有条件地将采样语义实现为目标参考轨迹上的残差形式效应。所得的上下文锚定正常-异常对应为判别性检测器学习提供时间监督。在九个数据集上的实验表明，CAPS在所有四个评估指标中相比其他方法实现了最强的综合性能，而互补的消融和迁移分析支持上下文锚定、语义解缠和条件实现的作用。
英文摘要
Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-dependent normal--anomalous distinctions. We propose Context-Anchored Pair Supervision (CAPS), a supervision-recovery framework for TSAD. CAPS views ideal anomaly supervision as a matched comparison between normal and anomalous outcomes under the same temporal context, and seeks to recover such supervision without target-domain anomaly labels. Using simulated normal--anomalous pairs, CAPS learns structure and anomaly-semantic representations through reconstruction, background consistency, and within-pair counterfactual recombination. The resulting anomaly representations form a continuous semantic space with coarse modes and induce a sampleable multimodal prior. CAPS conditionally realizes sampled semantics as residual-form effects on target reference trajectories. The resulting context-anchored normal--anomalous counterparts provide temporal supervision for discriminative detector learning. Experiments on nine datasets show that CAPS achieves the strongest aggregate performance across all four evaluation metrics among the compared methods, while complementary ablations and transfer analyses support the roles of context anchoring, semantic disentanglement, and conditional realization.
