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：本文提出了一种新的时间序列数据异常检测方法，通过将差异子空间的概念引入奇异谱分析（SSA）。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的差异子空间的细微时间变化作为异常分数进行监测。这是传统基于SSA方法（该方法将两个信号子空间之间的最小夹角作为变化程度的度量）的自然推广。通过用差异子空间替代最小夹角，我们的方法在保持SSA框架的同时提升了性能，因为它能够从幅度和方向上捕捉两个子空间之间的整体结构差异。通过在公开时间序列数据集上的性能评估，我们证明了该方法的有效性。