Abstract
This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series (DTS). In our method, there are three levels for mining similarity and periodicity patterns. At the first level, a structural-based search based on distance measure models is employed to find pattern structures; the second level performs a value-based search on the discovered patterns using local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of“Exchange Rates Patterns” between the U.S. dollar and the United Kingdom Pound.
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© 2001 Springer-Verlag Berlin Heidelberg
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Lin, W., Orgun, M.A., Williams, G.J. (2001). Temporal Data Mining Using Hidden Markov-Local Polynomial Models. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_35
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DOI: https://doi.org/10.1007/3-540-45357-1_35
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