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.
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。
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。
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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