Abstract
This paper proposed a new prediction method for outliers over data stream based on sparse representation to improve the optimum prediction speed and performance of outliers over data stream. Combining the wavelet noise detection method, using newly developed tools for sparse representation, a transformation method for outliers over data stream was proposed. In order to identify outliers, the introduction of random measurement matrix of wavelet transform coefficients was applied with sparse representation to forecast data value in the future timestamp. The simulation results on actual data source show that this method can provide precise instantaneous detection under certain conditions.
Published Version
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