A Multivariate Time Series Anomaly Detection Method Based on Clustered Particle Swarm Optimization

Abstract

Due to the advent of 5G and the integration of sensors, sensor nodes need to collect data from multiple sources simultaneously.Traditional anomaly detection methods for single attribute time series detect multivariate data, low detection accuracy, and significant node energy consumption. To avoid these problems, this paper provides an improved FCM multivariate time series clustering method based on the sliding window. Based on the CPSO optimal weight, the reconstruction error realizes anomaly detection. Experiments on different datasets demonstrate the effectiveness of the algorithm. Moreover, compared with the three classical methods, the results show that the proposed algorithm has higher detection accuracy and accuracy.