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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