基于多维灰色模型及神经网络的销售预测

在时尚销售领域，如服饰、手袋、钱包等，准确的销售预测对企业非常重要.然而由于客户的需求受诸多因素的影响，要做到准确的销售预测一直是一个富有挑战性的问题.基于改进的多维灰色模型（GM（1，N））和神经网络（ANN）提出一种混合模型来预测销量，其中多维灰色模型对销售数据建模，神经网络对误差进行校正.该混合模型的优点是考虑了影响客户需求的因素与销量之间的关系.通过对阿里天猫销售数据来评估混合模型的表现，实验结果表明，所提出的混合模型的预测结果要优于其他几种销售预测模型.

Sales Forecasting Based on Multi-dimensional Grey Model and Neural Network

Accurate sales forecasting is important to the fashion enterprise, such as apparel and accessories, handbags, wallets. However, it is a challenging problem since the requirements from consumers can be influenced by many factors. In this paper, the sales are forecasted based on an improved multidimensional grey model (IGM(1,N)) and artificial neural network (ANN), where the multi-dimensional grey model is used to model sales data while the neural network is used to correct the errors. The advantage of the proposed hybrid model is that it considers the relation between the sales and the factors that influence the customer requirements. The performance of the proposed hybrid model is evaluated with sales data from Ali-TianMao, and the experimental results demonstrate that the proposed hybrid model is superior to the existing sales forecasting models.