This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA). The key idea is to monitor slight temporal variations of the difference subspace between two signal subspaces corresponding to the past and present time-series data, as anomaly score. It is a natural generalization of the conventional SSA-based method which measures the minimum angle between the two signal subspaces as the degree of changes. By replacing the minimum angle with the difference subspace, our method boosts the performance while using the SSA-based framework as it can capture the whole structural difference between the two subspaces in its magnitude and direction. We demonstrate our method's effectiveness through performance evaluations on public time-series datasets.

翻译：本文提出了一种通过将差分子空间的概念融入奇异谱分析（SSA）的时间序列数据异常检测新方法。其核心思想是将过去与当前时间序列数据对应的两个信号子空间之间差分子空间的微弱时间变化作为异常评分进行监测。该方法是对传统基于SSA的方法的自然推广——传统方法将两个信号子空间的最小夹角作为变化程度的度量。通过用差分子空间替代最小夹角，本方法在沿用SSA框架的同时，因其能从幅度和方向两个维度捕捉两个子空间间的整体结构差异，从而提升了检测性能。我们通过在公开时间序列数据集上的性能评估，验证了该方法的有效性。