摘要
根据移动通信话务量的时间序列,采用基于模拟退火(SA)算法对超参数选择的支持向量回归机(SVR)进行建模预测。比较ARIMA、人工神经网络和SVR3种模型的预测效果,并对比研究网格法、遗传算法和SA3种SVR超参数选择方法对预测效果的影响。实验结果表明,SA-SVR预测精度高、耗时少,是一种预测移动通信话务量的有效方法。
According to the time series of mobile communications traffic, this paper adopts Support Vector Regression（SVR） model which choses its hyper-parameters based on Simulated Annealing（SA） algorithms. It compares the forecasting effects of ARIMA, ANN and SVR. The influence on traffic forecasting from three hyper-parameter selection methods such as grid, GA and SA is comparatively studied. Experimental results show that the forecasting of SA-SVR is accurate and less time-consuming, it is an effective method of forecasting mobile communication traffic.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第22期195-196,199,共3页
Computer Engineering
基金
中国移动新疆分公司研究发展基金资助项目
关键词
移动通信话务量预测
模拟退火算法
支持向量回归机
mobile communication traffic forecasting
Simulated Annealing（SA） algorithms
Support Vector Regression（SVR）
