中文摘要：

针对目前移动通讯对话务量预测的高精度、高效率和多步预测需求,提出一种基于最小二乘支持向量机（least-squaresupport vector machine,LS-SVM）的话务量预测方法,采用自相关分析法确定LS-SVM建模输入样本的嵌入维数和延迟时间,最大限度地保留历史信息并降低样本的维数;在此基础上,以最少量预测值代替真实值构成多步预测的输入样本,解决了多步预测精度下降的问题。通过中国移动黑龙江有限公司完成的实际应用测试表明：该方法可以实现话务量的高精度、在线多步预测,具备良好的实用性。

英文摘要：

Aiming at the actual requirements of high precision,high efficiency and multi-step forecasting of mobile communication traffic,this paper proposes a forecasting method based on LS-SVM.Self correlation analysis is adopted to determine the embedding dimension and delay time of the input vectors of LS-SVM,which maximally preserves historic information and reduces sample dimension.Besides,the input vectors are constructed with least forecasted values that substitute for real values,and multi-step forecasting is realized with high precision.The developed forecasting system software was applied in the network management system in Heilongjiang Co.Ltd.,China Mobile Communications Corporation（CMCC）.Test results with real communication traffic data indicate that the proposed method can realize real-time forecasting of mobile communication traffic with high precision and high efficiency,which is valuable for improving call quality.