作　　者： (李文璟）; (陈晨）; (喻鹏）; (熊翱）;

机构地区： 北京邮电大学网络与交换技术国家重点实验室

出　　处： 《北京邮电大学学报》 2017年第S1期10-14,共5页

摘　　要： 针对当前业务量预测方法过于理想化、预测准确度不高等问题,根据现网业务量特征提出了一种基于乘积季节自回归求和移动平均(S-ARIMA)模型的业务量预测方法.依据现网业务量的特征,详细分析了基于S-ARIMA的业务量预测建模的数学过程,经过现网大量业务量数据验证,S-ARIMA模型相比其他模型方法在预测值和置信区间上均具有较好的结果,是一种合理有效的业务量预测方法. During the study of the technologies of energy saving,how to ensure the changing trend of traffic accurately is a prerequisite of many energy-saving technology. Contraposing the current methods for traffic prediction being a bit idealistic and the low accuracy prediction,we propose a traffic prediction method based on the seasonal autoregressive integrated moving average( S-ARIMA) model in view of the traffic character in the network and implement it. According to the characteristics of the traffic character in the network,We analyze the mathematical process of the S-ARIMA mode detailedly. It is tested by a lot of traffic data in the wireless communication networks and the results indicate that for prediction values and confidence intervals S-ARIMA model performs better than other models. Therefore,this traffic prediction for wireless communication networks using S-ARIMA model is reasonable and efficient.

关 键 词： 模型 时间序列 无线通信网 业务量预测