VACE：学习几何结构化表示用于时间序列异常检测
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
AI总结：
提出VACE方法，通过速度一致性自监督学习生成紧凑且方向一致的嵌入空间，结合马氏距离位置得分和速度库方向得分实现多变量时间序列异常检测，在TSB-AD-M基准上达到最优性能。
摘要：
多变量时间序列中的异常检测是广泛实际应用中的关键任务，其中异常行为罕见、标签不可用且漏检成本高昂。核心挑战在于学习足够精确的正常性表征以标记偏差。表示自监督学习（通常通过对比方法）通过将时间补丁嵌入到潜在空间来解决这一问题，其中正常性占据一个定义明确的区域，异常通过几何偏差检测。然而，对比方法通过配对采样启发式间接塑造该空间，无法对基于距离评分所需的几何结构进行显式控制。这意味着正常表示的紧凑程度以及距离是否具有方向意义。我们提出VACE（速度对齐通道嵌入），一种自监督异常检测方法，将正常性表示为嵌入空间中紧凑且方向一致的区域。为此，VACE通过速度一致性目标训练通道感知编码器，无需负样本和合成异常，使得正常轨迹局部平滑且对齐。在测试时，马氏距离位置得分和速度库方向得分相乘，标记同时偏离分布和动态异常的点。尽管方法简单，VACE在严格评估下于TSB-AD-M上实现了最先进性能，显著优于使用更大预算训练的复杂方法。
英文摘要：
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the cost of a miss is high. The central challenge is learning a characterisation of normality precise enough to flag deviations. Representation self-supervised learning, typically through contrastive approaches, addresses this by embedding temporal patches into a latent space where normality occupies a well-defined region, with anomalies detected by geometric deviation. However, contrastive approaches shape this space indirectly through pair-sampling heuristics, providing no explicit control over the geometric structure that distance-based scoring requires. This means how tightly normal representations are grouped, and whether distances are directionally meaningful. We present VACE (Velocity-Aligned Channel Embeddings), a self-supervised anomaly detection method that represents normality as a compact, directionally coherent region in the embedding space. To this end, VACE trains a channel-aware encoder through a velocity-consistency objective, with no negatives and no synthetic anomalies, so that normal trajectories are locally smooth and aligned. At test time, a Mahalanobis positional score and a velocity-bank directional score are combined multiplicatively, flagging points that are simultaneously off-distribution and dynamically atypical. Despite its simplicity, VACE achieves state-of-the-art performance on TSB-AD-M under rigorous evaluation, significantly outperforming more complex methods trained on substantially larger budgets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
