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Symmetry 2018, 10(4), 113; doi:10.3390/sym10040113

Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

1,2,* , 1,* , 1
and
1
1
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
2
The 22th Research Institute of China Electronics Technology Group Corporation, Xinxiang 453003, China
*
Authors to whom correspondence should be addressed.
Received: 9 April 2018 / Revised: 9 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
View Full-Text   |   Download PDF [1481 KB, uploaded 17 April 2018]   |  

Abstract

Since data stream anomaly detection algorithms based on sliding windows are sensitive to the abnormal deviation of individual interference data, this paper presents a sliding nest window chart anomaly detection based on the data stream (SNWCAD-DS) by employing the concept of the sliding window and control chart. By nesting a small sliding window in a large sliding window and analyzing the deviation distance between the small window and the large sliding window, the algorithm increases the out-of-bounds detection ratio and classifies the conceptual drift data stream online. The designed algorithm is simulated on the industrial data stream of drilling engineering. The proposed algorithm SNWCAD is compared with Automatic Outlier Detection for Data Streams (A-ODDS) and Distance-Based Outline Detection for Data Stream (DBOD-DS). The experimental results show that the new algorithm can obtain higher detection accuracy than the compared algorithms. Furthermore, it can shield the influence of individual interference data and satisfy actual engineering needs. View Full-Text
Keywords: data stream; nested sliding window; anomaly detection; machine learning; concept drift; control chart data stream; nested sliding window; anomaly detection; machine learning; concept drift; control chart
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Li, G.; Wang, J.; Liang, J.; Yue, C. Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection. Symmetry 2018, 10, 113.

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