Earticle

초록

영어

Dynamic time warping algorithm (DTW) is a method of measuring the similarity of
time series. Concerning the problem that DTW cannot keep high classification accuracy
when the computation speed improved, a FG-DTW method based on the idea of naive
granular computing is proposed. In this method, firstly, better temporal granularity is
acquired by calculating temporal variance feature and it is used to replace original time
series; Secondly, the elastic size of under comparing time series granularity allow
dynamic adjustment through DTW algorithm and optimal time series corresponding
granularity is obtained; Finally, DTW distance is calculated by optimal corresponding
granularity model. At the same time, the early termination strategy of infimum function is
introduced to improve the efficiency of FG-DTW algorithm. Experiments show that the
proposed algorithm improves the running rate and accuracy effectively.

목차

Abstract
 1. Introduction
 2. Dynamic Time Warping Similarity Algorithm
 3. Multi-granularity DTW Model
  3.1. Granulation Partition Based on Time Series Variance
  3.2. The Optimal Coarse Grain Size Division Model
  3.3. FG-DTW Synthesis Algorithm
 4. Experimental Program
  4.1. Introduction of Experiment
  4.2. Experiment One
  4.3. Experiment Two
 5. Conclusion
 References

키워드

저자

• Xu Jianfeng [ College of Software, Nanchang University, Nanchang, China / Department of Computer Science, TongJi University, Shanghai, China ]
• Tang Tao [ College of Software, Nanchang University, Nanchang, China ]
• Zhang Yuanjian [ Department of Computer Science, TongJi University, Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605