用噪声约束提升时间序列异常检测的稳定性
COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
- 训练时引入高斯白噪声正则化，让重建残差符合理想分布
- 通过自适应卡尔曼平滑器降低异常分数噪声，提升可靠性
- 不依赖特定模型，可通用增强主流检测方法性能
基于重构的时间序列异常检测（TSAD）普遍采用均方误差（MSE）损失，导致重构残差存在统计缺陷，引发噪声大、波动强的异常分数，影响检测可靠性。为此，本文提出通用且模型无关的增强框架COGNOS，从根源解决该问题。COGNOS在训练阶段引入新型高斯白噪声正则化策略，直接约束模型输出残差服从高斯白噪声分布。这一设计为第二项贡献——自适应残差卡尔曼平滑器——创造了理想的统计条件，该平滑器作为稳健估计器，可有效去除原始异常分数中的噪声。在多个基准数据集上的大量实验表明，COGNOS能显著提升现有先进模型的性能，验证了统计正则化与自适应滤波结合的有效性。
原文摘要 · Abstract (English)
Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean Squared Error (MSE) loss results in statistically flawed reconstruction residuals. This fundamental weakness leads to noisy, unstable anomaly scores, hindering reliable detection. To address this, we propose Constrained Gaussian-Noise Optimization and Smoothing (COGNOS), a universal, model-agnostic enhancement framework that tackles this issue at its source. COGNOS introduces a novel Gaussian-White Noise Regularization strategy during training, which directly constrains the model's output residuals to conform to a Gaussian white noise distribution. This engineered statistical property creates the ideal precondition for our second contribution: Adaptive Residual Kalman Smoother that operates as a statistically robust estimator to denoise the raw anomaly scores. Extensive experiments on multiple benchmarks demonstrate that COGNOS consistently enhances the performance of state-of-the-art backbones significantly, validating the efficacy of coupling statistical regularization with adaptive filtering.
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