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...

Full description

Saved in:

Bibliographic Details
Published in:	2017 Seventh International Conference on Innovative Computing Technology (INTECH) pp. 36 - 41
Main Authors:	Jin Zhang, Xiaohui Zhu, Yong Yue, Wong, Prudence W. H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2017
Subjects:	anomaly data detection autoregressive linear combination model backtracking verification Computers Conferences dual time-moving windows
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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