基于聚类和支持向量机的话务量预测模型  

Traffic Forecasting Model Based on Clustering and Support Vector Machine

作　　者：陈电波[1] 徐福仓[1] 吴敏[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《控制工程》2009年第2期195-198,共4页Control Engineering of China

基　　金：国家杰出青年科学基金资助项目(60425310)

摘　　要：针对利用单因素时间序列模型进行话务量预测的不足,建立基于模糊C均值(FCM)聚类和支持向量机(SVM)的多元回归话务量预测模型。模型使用FCM算法对话务量的原始样本集聚类,选择与待预测样本特征最相似的样本子集作为训练集,使用SVM训练样本,通过决策回归函数预测话务量。实际话务量数据验证表明,该方法较周期时间序列和神经网络预测方法具有更高的预测精度和泛化能力。A novel multiple regression traffic forecasting model based on the fuzzy C-means （ FCM ） clustering and the support vector machine （SVM） is presented, to handle the insufficiency brought by the single-factor time series model for telephone traffic forecasting. FCM is used to cluster the original sample set, so that the subset, whose character is the most similar to the sample set to be forecasted, is chosen from the sample set. Then by applying SVM, the decision function is given, to forecast telephone traffic. The verification on the model with real telephone traffic data shows that, comparing with cycle time series and neural network forecast methods, the model performs higher forecasting accuracy and better generalization ability.

关 键 词：话务量 预测模型 模糊C均值聚类 支持向量机 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]