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Enhancing DWT for Recent-Biased Dimension Reduction of Time Series Data

  • Yanchang Zhao
  • Chengqi Zhang
  • Shichao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

Abstract

In many applications, old data in time series become less important as time elapses, which is a big challenge to traditional techniques for dimension reduction. To improve Discrete Wavelet Transform (DWT) for effective dimension reduction in this kind of applications, a new method, largest-latest-DWT, is designed by keeping the largest k coefficients out of the latest w coefficients at each level of DWT transform. Its efficiency and effectiveness is demonstrated by our experiments.

Keywords

Time series dimension reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanchang Zhao
    • 1
  • Chengqi Zhang
    • 1
  • Shichao Zhang
    • 1
  1. 1.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia

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