| 大型呼叫中心人工呼入量的最小二乘支持向量机模型 |
| |
| 引用本文： | 李大川,忻展红. 大型呼叫中心人工呼入量的最小二乘支持向量机模型[J]. 控制理论与应用, 2009, 26(7): 815-818 |
| |
| 作者姓名： | 李大川 忻展红 |
| |
| 作者单位： | 北京邮电大学,经济管理学院,北京,100876;中国移动通信集团,北京,100032;北京邮电大学,经济管理学院,北京,100876 |
| |
| 基金项目： | 国家自然科学基金资助项目(70473006) |
| |
| 摘 要： | 通过分析大型呼叫中心人工呼入量的数据特点, 文中将呼入量分解为日呼入量与相应时间段呼入量, 利用最小二乘支持向量机(LS-SVM)的原理, 建立日呼入量与时间段呼入量两个时间序列预测模型. 实验仿真证明, 采用该方法建立的日呼入量与时间段呼入量预测模型, 在回归和预测方面都可以得到满意的结果. 通过与神经网络预测模型的对比分析, LS-SVM总体上优于人工神经网络的预测效果.
|
| 关 键 词： | 呼叫中心 预测 最小二乘支持向量机 |
| 收稿时间： | 2008-06-11 |
| 修稿时间： | 2008-09-18 |
Large call-centers'arrival-rates prediction models based on the least squares support vector machine |
| |
| LI Da-chuan and XIN Zhan-hong. Large call-centers'arrival-rates prediction models based on the least squares support vector machine[J]. Control Theory & Applications, 2009, 26(7): 815-818 |
| |
| Authors: | LI Da-chuan and XIN Zhan-hong |
| |
| Affiliation: | School of Economics and Management, Beijing University of Posts and Telecommunications,School of Economics and Management, Beijing University of Posts and Telecommunications |
| |
| Abstract: | In analyzing the data from a large call center, we find that arrival rates can be split into the daily-arrival-rate and the time-period-arrival-rate. Based on the least squares support vector machine theory(LS-SVM), predicting models of the daily-arrival-rate and the time-period-arrival-rate are established. Simulation experiments show that these models are good at regression and forecasting. Compared with Back-Propagation(BP) neural network prediction models, these models give better prediction results. |
| |
| Keywords: | call center forecasting least squares support vector machine theory(LS-SVM) |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《控制理论与应用》浏览原始摘要信息 |
|
点击此处可从《控制理论与应用》下载全文 |
|
