A real-time anomaly detection algorithm/or water quality data using dual time-moving windows
Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical pattern...
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| Veröffentlicht in: | 2017 Seventh International Conference on Innovative Computing Technology (INTECH) S. 36 - 41 |
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01.08.2017
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| Abstract | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical patterns in real-time. The algorithm is based on an autoregressive linear combination model, a prediction interval with dual time-moving windows and a backtracking verification strategy. We have tested the algorithm using 3-month water quality data of PH from a real water quality monitoring station in a river system. Experimental results show that our novel anomaly detection algorithm can significantly decrease the rate of false positive and has better anomaly detection performance than AD and ADAM algorithms. |
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| AbstractList | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical patterns in real-time. The algorithm is based on an autoregressive linear combination model, a prediction interval with dual time-moving windows and a backtracking verification strategy. We have tested the algorithm using 3-month water quality data of PH from a real water quality monitoring station in a river system. Experimental results show that our novel anomaly detection algorithm can significantly decrease the rate of false positive and has better anomaly detection performance than AD and ADAM algorithms. |
| Author | Jin Zhang Wong, Prudence W. H. Yong Yue Xiaohui Zhu |
| Author_xml | – sequence: 1 surname: Jin Zhang fullname: Jin Zhang email: sgjzha31@liverpool.ac.uk organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 2 surname: Xiaohui Zhu fullname: Xiaohui Zhu email: bobzhu@liverpool.ac.uk organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 3 surname: Yong Yue fullname: Yong Yue email: yong.yue@xjtlu.edu.cn organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 4 givenname: Prudence W. H. surname: Wong fullname: Wong, Prudence W. H. email: pwong@liverpool.ac.uk organization: Dept. of Comput. Sci., Univ. of Liverpool, Liverpool, UK |
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| Snippet | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a... |
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| SubjectTerms | anomaly data detection autoregressive linear combination model backtracking verification Computers Conferences dual time-moving windows |
| Title | A real-time anomaly detection algorithm/or water quality data using dual time-moving windows |
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