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• W2078631608 abstract "A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known curse of dimensionality problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretize the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences (motifs) as features." @default.
• W2078631608 created "2016-06-24" @default.
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• W2078631608 date "2014-07-12" @default.
• W2078631608 modified "2023-10-05" @default.
• W2078631608 title "SAX-EFG" @default.
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• W2078631608 doi "https://doi.org/10.1145/2576768.2598321" @default.
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