Parameter optimisation of sliding window algorithm based on ensemble multi-objective evolutionary computation

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.

Automated summary

In this study, a sliding window-based method for parameter optimisation of data stream trend anomaly detection algorithm is proposed. The method treats data stream anomaly detection as a two-objective optimisation problem and uses three optimisation algorithms and ensemble strategies to obtain the optimal parameter settings. Through verification of multiple real parameter data, it is found that this method can achieve optimal parameter settings and provide a reference for the parameter setting of data stream trend anomaly detection algorithm based on sliding window.