Existing dimension reduction method for multivariate time series can’t preserve their feature effectively.
Therefore, the drawback of PCA method is analyzed, when it is used in MTS dimension reduction, and based on common
principal component analysis, a dimension reduction method for multivariate time series is proposed. The computational
complexity and the validity of dimension reduction are compared between different methods. The results of experiments
show that the proposed method can reduce dimension effectively at comparatively low computational cost, and at the same
time preserve most feature of multivariate time series.