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A new method of real-time detection of changes in periodic data stream
Author(s): Chen Lyu; Guoliang Lu; Bin Cheng; Xiangwei Zheng
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Paper Abstract

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.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200U (21 July 2017); doi: 10.1117/12.2281699
Show Author Affiliations
Chen Lyu, Shandong Normal Univ. (China)
Guoliang Lu, Shandong Univ. (China)
Bin Cheng, North Automatic Control Technology Institute (China)
Xiangwei Zheng, Shandong Normal Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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