This paper proposes an effective timeseries matching method by combining the empirical mode decomposition (EMD) with the alternative covering algorithm.
将经验模式分解和多层前向网络的交叉覆盖算法相结合,提出一种时间序列相似模式的匹配算法。
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Nonlinear and nonstationary timeseries are decomposed into a series of instrinsic mode functions and a residual trend item by the empirical mode decomposition (EMD).
非线性,非平稳的时间序列经过经验模分解,可以得到一组内模函数和一个基本的趋势项。
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Conclusion Timeseriesdecomposition is applicable of drug consumption prediction of h...