关键词: 自回归整合滑动平均（ARIMA）, 乘积季节ARIMA, 自相关函数, 相关系数, 话务量

Abstract: Traffic prediction is of significant importance for telecommunication network construction plan and network optimization.The traffic series for a province of a mobile network operator is studied and the regularities about trend and season are explored using autocorrelation function.Based on the characteristics exhibited in the traffic series，multiplicative seasonal Autoregressive Integrated Moving Average model（ARIMA） is employed to make traffic series prediction.Daily traffic per month for the province and for every region from July to December in 2008 is forecasted，and it is compared with the actual operation data.The Mean Absolute Percentage Error（MAPE） for one-step ahead prediction is 1.382%，and MAPE for the 6 steps are all less than 6%.The prediction result is of high precision.Furthermore，the cause for the large prediction error in 2 regions is analyzed，and the appropriateness of the model is testified on the opposite aspect.This paper also provides an effective method by using correlation coefficients for analyzing the reason for large prediction errors which often happen in time series prediction applications.

Key words: Autoregressive Integrated Moving Average（ARIMA）, multiplicative seasonal Autoregressive Integrated Moving Average, autocorrelation function, correlation coefficient, traffic