Outlier Detection of Multivariate Time Series Based on Weighted Euclid Norm
Guo Xiao-fan · 2014
In order to improve the precision of anomaly detection algorithm for multivariate time series,based on the cumulative contribution rate of principal components,sequence and its principal components are selected,and in the k nearest neighbor local outlier detection algorithm,the weighted Euclid norm distance is used as the k nearest neighbor distance,so as to realize the anomaly detection of multivariate time series.In order to verify the effectiveness of the algorithm,anomaly detection was carried out on test data.The experimental results show that the algorithm has more advantages than traditional methods in the precision and recall.