Nonlinear and nonstationary time series 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。
Based on decomposition and reconstruction of the signals by filter sets, the trend-signal is separated from stationary stochastic signals.
这种方法通过滤波器组将信号分解与重构，实现趋向性信号与零均值平稳随机信号的分离。
In this paper, according to the characteristics of non linear network traffic, traffic behaviors are decomposed into trend items, period items, and random items by an accumulation decomposition model.
本文采用一种累加模型将复杂大规模网络流量分解成趋势项、周期项和随机项。
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