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。

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.

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文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。

According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.

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首先利用EMD方法对NYMEX市场的原油期货价格进行分解，得到一系列平稳且具有较强周期性的本征模函数(IMF)和一个趋势项。

First we employ EMD method to decompose the crude oil prices in NYMEX, and obtain a series of intrinsic mode function (IMF), which are stationary and of strong periodicity, and a trend term.

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首先利用EMD方法对NYMEX市场的原油期货价格进行分解，得到一系列平稳且具有较强周期性的本征模函数(IMF)和一个趋势项。

First we employ EMD method to decompose the crude oil prices in NYMEX, and obtain a series of intrinsic mode function (IMF), which are stationary and of strong periodicity, and a trend term.

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