LI Quan1,2, ZHOU Xing-she1

(1. School of Computer Science, Northwestern Polytechnic University, Xi′an 710072, China;

2. Xi′an Satellite Control Center, Xi′an 710043, China)

Abstract: A new method of detecting anomalies in MTS (multivariate time series) is introduced, in which a similarity matrix for MTS is set up and the similarity matrix is transformed to maximize the correlation between the data points and then the anomalous data points are detected by comparing the predefined threshold with the connectivity coefficient calculated through the random walk model. This detection method makes full use of the correlation between the data points and effectively reduces the influence of the noise. The omission rate and false alarms decrease obviously, and the simulation has tested and verified the validity of this method.

Key words: MTS(multivariate time series); anomalies detection; similarity analysis