基于神经网络的客服中心话务量预测模型

针对现有预测模型在话务量发展趋势变化、新技术新业务引入后模型失效、预测精度下降等问题，提出一种基于神经网络和事件样本库的智能预测方法。该方法具有自学习功能，可根据预测误差自动调整预测参数并更新事件样本，对话务量趋势变化、事件影响程度变化及新事件的发生具有持续自适应能力。仿真结果表明，该预测方法能有效降低预测误差，与现有方法相比，话务量的预测精度提高了6．57％。

Neural Network Based Traffic Prediction Model of Customer Service Center

An intelligent prediction model based on neural network and event sample database is proposed to solve the changes of traffic trends and the model failure after the introduction of new technologies and new business and decrease of prediction accuracy. The method has self-learning function and continuing adaptive capacity on changes of traffic trends, events influence and the occurrence of new events, which can automatically adjust predictive parameters and update the event samples according to predictive error. Simulation results show that the method effectively improves the accuracy of traffic prediction, also has a high value for investment planning of the company＇s customer service center.