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.
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Fu, Tc., Law, Cw., Chan, Kk., Chung, Fl., Ng, Cm. (2006). Stock Time Series Categorization and Clustering Via SB-Tree Optimization. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_141
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DOI: https://doi.org/10.1007/11881599_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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