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.
利用随机过程的方法来分析系统误差的变化过程，建立了系统误差的随机过程模型——误差累加模型和维纳过程模型。
Rules of the system error along with time is represented and stochastic process models are established: error accumulating model and Wiener model.
实例表明，一次累加法预测模型精度高，预测结果可靠，可用于城市燃气年负荷预测。
The practical example shows that the forecast model based on once accumulation method has high precision and reliable result, it can be used to forecast the annual city gas load.
应用推荐