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.
CITATION STYLE
Lin, W., Orgun, M. A., & Williams, G. J. (2000). Temporal data mining using multilevel-local polynomial models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1983, pp. 180–186). Springer Verlag. https://doi.org/10.1007/3-540-44491-2_27
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