TY - GEN
T1 - A real-time anomaly detection algorithm/or water quality data using dual time-moving windows
AU - Zhang, Jin
AU - Zhu, Xiaohui
AU - Yue, Yong
AU - Wong, Prudence W.H.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/8
Y1 - 2017/11/8
N2 - 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.
AB - 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.
KW - anomaly data detection
KW - autoregressive linear combination model
KW - backtracking verification
KW - dual time-moving windows
UR - http://www.scopus.com/inward/record.url?scp=85040786414&partnerID=8YFLogxK
U2 - 10.1109/INTECH.2017.8102421
DO - 10.1109/INTECH.2017.8102421
M3 - Conference Proceeding
AN - SCOPUS:85040786414
T3 - 7th International Conference on Innovative Computing Technology, INTECH 2017
SP - 36
EP - 41
BT - 7th International Conference on Innovative Computing Technology, INTECH 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Innovative Computing Technology, INTECH 2017
Y2 - 16 August 2017 through 18 August 2017
ER -