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,该方法将差异子空间的概念引入奇异谱分析(SSA)中。其核心思想是将过去和当前时间序列相对应的两个信号子空间的差异子空间微小的时间变化作为异常得分监控。它是传统SSA方法的自然推广,传统方法通过将两个信号子空间的最小角度作为变化程度进行测量。通过用差异子空间代替最小角度,我们的方法在使用SSA框架的同时提高了性能,因为它可以捕捉两个子空间之间整个结构差异的大小和方向。我们通过公共时间序列数据集上的性能评估证明了我们方法的有效性。