JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (3): 51-57.doi: 10.6040/j.issn.1672-3961.2.2015.050

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Time series classification using piecewise vector quantized approximation based on Mahalanobis distance

TAO Zhiwei1, ZHANG Li1,2*   

  1. 1. School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China;
    2.Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, Jiangsu, China
  • Received:2015-05-14 Online:2016-06-30 Published:2015-05-14

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Abstract: A Mahalanobis distance-based time series classification using PVQA(MPVQA)algorithm was developed. On the basis of inheriting the time complexity of the traditional algorithm and by exploiting Mahalanobis distance, the algorithm could easily overcome the default that the Euclidean distance was easily influenced by the mode characteristic dimension and improve the accuracy. PVQA was first used to generate a codebook using training samples, and then the Mahalanobis distance was taken as the measure of similarity and used to reconstruct time subsequences. For an unseen time series, the Mahalanobis distance was also adopted to find the most similar one to it. Experimental results on four time series datasets demonstrated that our method was more powerful to classify the time series.

Key words: time series, piecewise vector quantized approximation, reconstruct, Mahalanobis distance, codebook, characteristic dimension, Euclidean distance

CLC Number: 

  • TP302.7
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