Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 482-487.

• Big Data & Data Mining • Previous Articles     Next Articles

Boundary Distance Algorithm for Determining Sliding Window Size

PENG Cheng1,2, HE Jing1, CHI Hao1   

  1. School of Computer,Hunan University of Technology,Zhuzhou,Hunan 412007,China1;
    School of Automation,Central South University,Changsha 410083,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: Due to a large amount of information and high density of the original measurement data collected by most equipment,the existing time series sliding window dimension reduction method uses the empirical value to determine the window size,which cannot retain important information points of the data to the utmost extent,and has high computational complexity.To this end,the influence of sliding window on time series similarity technology in practical applications was discussed,and an algorithm for determining the initial scale of sliding window was proposed.The upper and lower boundary curves with higher fitting degree are constructed,and the trend weighting is introduced into the LB_Hust distance calculation method,which reduces the difficulty of mathematical modeling and improves efficiency of equipment data similarity classification and state evaluation.

Key words: Data mining, LB_Hust distance, Sliding window

CLC Number: 

  • TP301
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