基于小波变换和PSO-BP的物流联络中心的任务量预测

Tasks Prediction in Contact Centers of Logistic Based on the Wavelet Transform and PSO-BP

随着物流快递等业务的迅速发展,联络中心作为一个新兴服务行业也随之变成了服务机构和客户沟通的最重要的桥梁。联络中心任务量预测的准确性对基础设施和人员投入至关重要。因此,文中提出了一种结合小波变换和PSO-BP的组合预测模型,通过小波变换把任务量序列分解成高频和低频序列,再为分解序列建立合适的PSO-BP预测模型,求出最优解。最后,实例分析表明,该模型对非线性时间序列有更好的拟合能力和更高的预测精度。

With the rapid development of logistic,express delivery and so on,the contact center,as a new service industry,has become the most important bridge between the service organizations and their customers. The accuracy of tasks prediction in contact centers of logistic is very important to infrastructure investment and staffing. Therefore,a model that combines the wavelet transform and PSO- BP neural network is proposed. By the wavelet transform,the tasks are decomposed into high frequency and low frequency series,for which the suitable PSO- BP models are established to search the optimal solution. Finally,the analysis of the example indicates that the fitting ability and prediction accuracy of the method are better than other methods.