基于直觉模糊时间序列与Elman神经网络组合模型的动态顾客需求预测

Based on intuition fuzzy time series and Elman neural network combination model of dynamic customer demand forecast

针对顾客动态需求预测问题,选取芥末留学咨询服务企业2019年4月至8月的顾客需求数据,采用Matlab7.0对芥末留学顾客需求的相关影响因素作为预测因子进行预测。结果表明:组合预测模型有效地提高了预测精度,将预测精度误差降低到了2.6%,为顾客需求预测提供了新的组合模型;将组合模型应用到了留学服务顾客需求预测中,为留学服务行业制定顾客策略提供了指导。

In view of the problem of customer dynamic demand prediction,the customer demand data of Mustard overseas study consulting service enterprises from April to August 2019 are selected and Matlab7.0 is used to predict the relevant influencing factors of Mustard overseas study customer demand.The results show that the combination prediction model can effectively improve the prediction accuracy and reduce the prediction accuracy error to 2.6%,which theoretically provides a new combination model for customer demand prediction and improves the prediction accuracy.In a practical sense,the combination model is applied to the customer demand of overseas study service consultation,which provides guidance for the customer strategy of overseas study service industry.