中文摘要：

针对通用无线分组业务（GPRS）小区流量预测问题,对几种典型时序预测模型的性能进行了综合分析。在总结时序预测模型使用步骤的基础上,分析了自回归（AR）、自回归移动平均（ARIMA）和乘积季节自回归求和移动平均（ARIMA）模型的性能。首先,对GPRS小区流量的变化情况进行分析;再根据流量的自相关系数和偏相关系数,从不同的角度进行分析,分别得到了流量变化的AR模型和ARMA模型;进而利用小区流量以天为周期变化的特点,得到了流量变化的乘积季节ARIMA模型。最后根据GPRS小区历史流量数据,应用这三种模型预测将来某一时间的流量,并对模型性能进行比较研究。

英文摘要：

The performances of some classic time series prediction models were analyzed together concerning the traffic prediction of General Packets Radio Service（GPRS）cells.Based on summarizing the steps of prediction by time series model,the performances of Auto-Regression（AR）model,Auto-Regression Moving Average（ARMA）model,Auto-Regressive Integrated Moving Average（ARIMA）model and multiple seasonal ARIMA model were analyzed.At first,the traffic changes of GPRS cells were studied.Then the autocorrelation coefficient and partial correlation coefficient of traffic were analyzed from different angles,and the AR model and ARMA model of the GPRS cells traffic were proposed.Furthermore,according to cell traffic changes in one day cycle,the multiple seasonal ARIMA model of the GPRS cells traffic was proposed.At the end,with the historical data of traffic of a cell,the three models were applied to predict the traffic sometimes in the future,and comparative study of the prediction performances of the three models were made as well.