用全局字典增强模型，实现多变量时间序列异常检测的统一准则
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
- 基于全局字典的交叉注意力机制，捕捉全序列正常点共享特征
- 在5个真实数据集上达到当前最优无监督检测效果
- 方法具有强跨数据集迁移能力，适合工业时序异常检测场景
无监督多变量时间序列异常检测面临挑战，因需在不访问异常点的情况下构建紧凑的检测准则。现有方法主要依赖重建误差或关联差异，但均局限于局部子序列且时间窗口有限，难以形成统一的序列级检测标准。本文提出全局字典增强的Transformer（GDformer），采用改进的基于字典的交叉注意力机制，挖掘整个序列中所有正常点共享的全局表示。由此产生的交叉注意力图反映了各点与全局表示的相关性权重，自然导出基于表示相似性的检测准则。为进一步压缩检测边界，引入原型来捕捉正常点-全局相关权重的分布特性。GDformer在五个真实世界基准数据集上持续取得最优无监督异常检测性能。进一步实验验证了全局字典在不同数据集间具有优异的可迁移性。
原文摘要 · Abstract (English)
Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly points. The existing methods are mainly based on reconstruction error or association divergence, which are both confined to isolated subsequences with limited horizons, hardly promising unified series-level criterion. In this paper, we propose the Global Dictionary-enhanced Transformer (GDformer) with a renovated dictionary-based cross attention mechanism to cultivate the global representations shared by all normal points in the entire series. Accordingly, the cross-attention maps reflect the correlation weights between the point and global representations, which naturally leads to the representation-wise similarity-based detection criterion. To foster more compact detection boundary, prototypes are introduced to capture the distribution of normal point-global correlation weights. GDformer consistently achieves state-of-the-art unsupervised anomaly detection performance on five real-world benchmark datasets. Further experiments validate the global dictionary has great transferability among various datasets.
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