Wavelet matrix transform for time-series similarity measurement

  • Zhi-kun Hu (胡志坤)Email author
  • Fei Xu (徐 飞)
  • Wei-hua Gui (桂卫华)
  • Chun-hua Yang (阳春华)


A time-series similarity measurement method based on wavelet and matrix transform was proposed, and its anti-noise ability, sensitivity and accuracy were discussed. The time-series sequences were compressed into wavelet subspace, and sample feature vector and orthogonal basics of sample time-series sequences were obtained by K-L transform. Then the inner product transform was carried out to project analyzed time-series sequence into orthogonal basics to gain analyzed feature vectors. The similarity was calculated between sample feature vector and analyzed feature vector by the Euclid distance. Taking fault wave of power electronic devices for example, the experimental results show that the proposed method has low dimension of feature vector, the anti-noise ability of proposed method is 30 times as large as that of plain wavelet method, the sensitivity of proposed method is 1/3 as large as that of plain wavelet method, and the accuracy of proposed method is higher than that of the wavelet singular value decomposition method. The proposed method can be applied in similarity matching and indexing for lager time series databases.

Key words

wavelet transform singular value decomposition inner product transform time-series similarity 


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Copyright information

© Central South University Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • Zhi-kun Hu (胡志坤)
    • 1
    Email author
  • Fei Xu (徐 飞)
    • 1
  • Wei-hua Gui (桂卫华)
    • 2
  • Chun-hua Yang (阳春华)
    • 2
  1. 1.School of Physics Science and TechnologyCentral South UniversityChangshaChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina

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