Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
Experimental results show that the predicted results by use of the algorithm approximate well to real ones and are more accurate compared to algorithms based on the exponential average method.
实验结果表明，使用该算法进行预测得到的预测值接近真实值，准确性高于指数平均预测算法。
With the assumption that the component processing time is exponential distribution, components' average queueing time in the system was calculated by queueing theory.
在零件加工时间服从指数分布的假设下，根据排队论求出零件在系统中平均排队时间。
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