A new method of real-time detection of changes in periodic data stream
Chen Lyu, Guoliang Lu, Bin Cheng, Xiangwei Zheng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.