【作者】 谭艳峰；

【导师】 贾振红；

【作者基本信息】 新疆大学 ， 信号与信息处理， 2010， 硕士

【摘要】 移动通信话务量预测问题一直是各大移动通信运营商关注的焦点,它对于掌握系统容量、网络扩容等情况具有直接的指导意义。本文采用SA-SVR预测话务量,并且结合资费水平应用到话务量预测模型当中,提高了预测精度。文章的主要内容包括:1.分析并比较当前常用的各种时间序列分析和预测算法,用实验验证了本文采用模型的优势;2.基于SVR坚实的理论支持和很强的非线性拟合回归能力,采用SVR模型对移动通信话务量时间序列进行预测;3.分析讨论了在SVR预测模型中核参数选择问题的重要性以及各个核参数对模型的影响情况,比较了当前常用的几种SVR核参数选择方法,并且首次采用基于模拟退火算法进行核参数选择的SVR模型对话务量序列进行预测,取得了很好的预测效果;4.通过实验讨论和分析了嵌入维数在时间序列分析预测过程中的重要性,并结合实验结果关于序列嵌入维数的选择得出结论;5.首次提出基于资费-SASVR模型的话务量预测模型,它充分利用话务量数据与话务资费水平之间的关系提取出话务量序列的总趋势,对提取后的序列采用SASVR模型进行预测。实验结果表明此模型具有很好的预测效果。更多还原

【Abstract】 The forecasting of mobile telephone traffic has always been the major mobile operators’focus, it is of directive meaning to master the system capacity and network expansion and so on. SA-SVR is utilized to forecast mobile traffic in this paper, what’s more, the tariff levels are combined in the model which improved the prediction accuracy. The contents are included in the article as follows:1. The common algorithms of time series analysis and prediction are analyzed, and experiments prove the advantages of the model put forward in this paper;2. Based on SVR’s solid theoretical support and a strong ability of nonlinear regression fitting, the SVR model is used to forecast mobile traffic time series;3. This paper analyzes and discusses the influence of the various nuclear parameters selection methods of SVR on prediction model as well as the importance of selecting the nuclear parameters. Then the common methods of nuclear parameters selection are compared in this paper. For the first time, the traffic time series are predicted by the SVR mode with simulated annealing selecting nuclear parameters, which makes a very good prediction results;4. The importance of embedded dimension in the process of time series analysis and forecasting is discussed and analyzed through experiments in this paper, then the conclusions on the sequence’s embedding dimension are drawn based on the experimental results;5. The Tariff-SASVR model for traffic forecasting is proposed for the first time in this paper. It takes good advantages of the relationship between tariff level and traffic and then extracts the general trend of traffic. The residual sequence is predicted by SASVR. The results show that the model has a pretty good forecasting effect.更多还原