最后，建立了基于时间序列的二次指数平滑线性预测模型，进行商品销售趋势的分析，部分验证了本文的设计分析。
Finally built a two linear forecasting models based on smoothing of time queue about analysis of sale trend, and verified the design analysis of this paper partly.
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.
根据新的目标函数，设计了一种重要点和自底向上分割相结合的时间序列分段线性化趋势特征提取方法。
The existing algorithms to extract trend features based on time series piecewise linearization representation cannot extract completely correct basic trend features of time series.
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