基于深度学习的连锁超市销售预测研究

Research on Sales Forecast of Chain Supermarket Based on Deep Learning

在竞争日益激烈的连锁超市零售业中,企业不断采取调整产品特性的策略,以满足顾客的需求和偏好。虽然有些产品的生命周期很短,但是通过处理和分析公司数据库中收集和存储的大量历史数据,获得库存控制和产品采购的策略。探讨运用深度学习的方法来预测连锁超市行业未来季节的个别新产品的销售数据。模型的开发考虑产品的物理特性和领域专家的意见,从而使预测结果对连锁超市公司的采购决策更有辅助价值。

In the increasingly competitive supermarket chain retail industry,enterprises constantly adopt the strategy of adjusting product characteristics to meet the needs and preferences of customers.Although some products have a short life cycle,strategies for inventory control and product procurement are obtained by processing and analyzing the large amount of historical data collected and stored in the company's database.This paper discusses the application of deep learning method to predict the sales data of individual new products in the supermarket chain industry in the coming season.The development of the model takes into account the physical characteristics of the product and the opinions of the experts in the field,so that the prediction results are of more auxiliary value to the purchasing decisions of supermarket chain.