Abstract: In order to improve the efficiency of outlier model detection algorithm of multivariate time series(MTS),based on the k-nearest neighbor local outlier detection algorithm,the principal component analysis of the multivariate time series method for dimensionality reduction method was used to detect anomalies of multivariate time series model.The experimental results show that the proposed algorithm detects MTS outlier pattern series more accurately and more efficiently