Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation. (12th July 2022)

• Record Type: Journal Article
• Authors: Li, Guang; Wang, Jie; Liang, Jing; Yue, Caitong; Lou, Tai-shan
• Is Part Of: International journal of bio-inspired computation. Volume 19:Number 4(2022)
• Page Start: 228
• Page End: 237
• Publication Date: 2022-07-12
• Subjects: sliding window -- parameter optimisation -- anomaly detection -- evolutionary computation -- ensemble strategy; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

The parameters of sliding window algorithm are difficult to determine. Therefore, a sliding window-based method for parameter optimisation of data stream trend anomaly detection algorithm is proposed in this study. This method regards the data stream anomaly detection as a two-objective optimisation problem. Three optimisation algorithms and ensemble strategies were used to obtain the optimal parameter settings of the algorithm. With this strategy, it is no longer difficult to determine the parameters of the data stream trend anomaly detection algorithm based on the sliding window. Through verification of multiple real parameter data in Tarim Oilfield, it could be known that this method could realise the optimal parameter settings, which provides a reference for the parameter setting of the data stream trend anomaly detection algorithm based on sliding window.