Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 110-113.

• Intelligent Computing • Previous Articles     Next Articles

Time Series Similarity Based on Moving Average and Piecewise Linear Regression

FENG Yu-bo1,2,DING Cheng-jun1,GAO Xue1,ZHU Xue-hong1,LIU Qiang1   

  1. School of Mechanical Engineering,Hebei University of Technology,Tianjin 300130,China1
    Taihua Hongye Tianjin Robot Technology Research Institute Co.Ltd.,Tianjin 300130,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: Aiming at the problems that the Euclidean distance is sensitive to the anomaly data and the efficiency of the DTW distance algorithm is low,a time series similarity method based on the moving average and the piecewise linear regression was proposed.Firstly,the original variable-averaging algorithm and the piecewise linear regression are used to transform the original time series.The parameters of the piecewise linear regression (intercept and distance) are taken as the characteristics of the time series so that the feature extraction of the time series is realized,and the data is dimensioned.Then it calculated distance using the dynamic time bending distance.The performance of the method is similar tothat of DTW algorithm,but the proposed method is almost 96% higher in algorithm efficiency.The experimental results verify the effectiveness and accuracy of the method.

Key words: Dynamic time warping(DTW), Linear regression, Moving average, Time series

CLC Number: 

  • TP311.13
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