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Stock Time Series Categorization and Clustering Via SB-Tree Optimization

  • Tak-chung Fu
  • Chi-wai Law
  • Kin-kee Chan
  • Fu-lai Chung
  • Chak-man Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

SB-Tree is a data structure proposed to represent time series according to the importance of the data points. Its advantages over traditional time series representation approaches include: representing time series directly in time domain (shape preservation), retrieving time series data according to the importance of the data points and facilitating multi-resolution time series retrieval. Based on these benefits, one may find this representation particularly attractive in financial time series domain and the corresponding data mining tasks, i.e. categorization and clustering. In this paper, an investigation on the size of the SB-Tree is reported. Two SB-Tree optimization approaches are proposed to reduce the size of the SB-Tree while the overall shape of the time series can be preserved. As demonstrated by various experiments, the proposed approach is suitable for different categorization and clustering applications.

Keywords

Time Series Discrete Wavelet Transform Time Series Data Vertical Distance Class Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tak-chung Fu
    • 1
    • 2
  • Chi-wai Law
    • 1
  • Kin-kee Chan
    • 1
  • Fu-lai Chung
    • 1
  • Chak-man Ng
    • 2
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong
  2. 2.Department of Computing and Information ManagementHong Kong Institute of Vocational Education (Chai Wan)Hong Kong

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