• Title/Summary/Keyword: 불균형 Haar 웨이블릿 변환

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Time series representation for clustering using unbalanced Haar wavelet transformation (불균형 Haar 웨이블릿 변환을 이용한 군집화를 위한 시계열 표현)

  • Lee, Sehun;Baek, Changryong
    • The Korean Journal of Applied Statistics
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    • v.31 no.6
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    • pp.707-719
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    • 2018
  • Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.