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 temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of “Exchange Rates Patterns” between the U.S. dollar and Australian dollar.
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Lin, W., Orgun, M.A., Williams, G.J. (2000). Temporal Data Mining Using Multilevel-Local Polynomial Models. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_27
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DOI: https://doi.org/10.1007/3-540-44491-2_27
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