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.
最后总结了滞后非线性模型的研究现状及将来的发展趋势。
Finally, the status quo of hysteretic nonlinearity modeling is summarized and the research direction is predicted.
其线性模型主要为因子分析模型和趋势面分析模型，非线性模型为BP神经网络模型。
Linear models mainly include the factor analysis model and the trend surface model. And nonlinear models include BP neural network model.
其线性模型主要为因子分析模型和趋势面分析模型，非线性模型为BP神经网络模型。
Linear models mainly include the factor analysis model and the trend surface model. And nonlinear models include BP neural network model.
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