A Novel Discords Detector for Periodic Time Series Based on Weighted Spherical Single Means with Phase Shift
基于移相加权球面单簇聚类的周期时间序列异常检测

The traditional one-class classifiers are not suitable for detecting discords in periodic time series. A novel one-class classifier PS-WS1M-OCC is proposed in this paper. In our method, the phase problem in time series is solved by introducing phase shift into the clustering procedure. Meanwhile, a novel criterion for adaptively choosing threshold is proposed. In this way, the proposed classifier is insensitive to noise in the training set. Experimental results show that our PS-WSKM-OCC is more robust than the existing one-class classifiers when it is applied to the problem of discord detection in the periodic time series.