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Temporal data mining using hidden Markov-local polynomial models
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机译:使用隐马尔可夫局部多项式模型进行时间数据挖掘
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摘要
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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