The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

The effect of the application of machine learning on data streams is influenced by concept drift, drift deviation, and noise interference. This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window methods. This algorithm is named DCUSUM-DS (Double C...

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Bibliographic Details
Main Authors: Guang Li, Jie Wang, Jing Liang, Caitong Yue
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Symmetry
Subjects:
concept drift
machine learning
anomaly detection
nested sliding window
data stream
Online Access:http://www.mdpi.com/2073-8994/10/7/264

Internet

http://www.mdpi.com/2073-8994/10/7/264

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