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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2017 Seventh International Conference on Innovative Computing Technology (INTECH) s. 36 - 41
Hlavní autori: Jin Zhang, Xiaohui Zhu, Yong Yue, Wong, Prudence W. H.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.08.2017
Predmet:
anomaly data detection
autoregressive linear combination model
backtracking verification
Computers
Conferences
dual time-moving windows
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
DOI:10.1109/INTECH.2017.8102421