Research And Application About The Forecasting For The Convenience-store Chain's Sales Based On Deep Learning

With the vigorous development of new retail industry,chain convenience stores have the advantages of long business hours,small space and light asset model,which are becoming more and more popular among consumers.Driven by two rounds of consumer upgrading and new retailing,China's convenience store industry is being fully launched.Major retail giants have invested a lot of money to participate in it.New convenience stores such as Suning Store and Tianmao Store have sprung up and expanded rapidly.How to stand out in the fierce market competition is a question worthy of consideration.The location of each store in chain convenience stores is different,the surrounding environment is different,and the residents' demand for different goods will also vary.Therefore,the store scientifically and reasonably allocates the stock of store goods,prevents the overstock of goods,more in line with the residents' shopping needs,and improves brand awareness and market competitiveness.The accurate sales forecast of convenience stores can guide the back-end operation and make reasonable resource matching and Optimization in advance.Accurate forecasting of commodity sales can effectively guide the back-end operation of convenience stores,carry out reasonable inventory management,timely adjust commodity pricing strategies,meet the daily shopping needs of neighboring residents,and effectively improve brand awareness and market competitiveness.Thus,sales forecasting technology is the key to facilitate market competition in the industry.Chain convenience stores are widely distributed in the region,the variety of commodities,the complex seasonal impact,and the market demand is difficult to predict.These factors increase the difficulty of commodity sales forecasting.Through the analysis of the relevant research of references,it is found that the traditional regression prediction algorithm is not ideal for the prediction of retail sales affected by multiple factors.In order to improve the accuracy of the prediction of convenience store sales,based on the theory of machine learning and deep learning,the deep belief network(DBN),neural network(NN)and support vector regression(SVR)are applied.A new combination model of regression and prediction was established by combining the methods.In order to facilitate the historical sales data set of stores as the research object,pretreatment and feature extraction are carried out on the data.The combined model is compared with traditional neural network(NN),support vector regression(SVR)and deep belief network(DBN).The experimental results show that the combined model has better prediction effect.Finally,from the application point of view,this paper analyses the potential needs of convenience store users,and designs a convenience store sales forecast system,including system architecture design,function design and database design.Through related technologies,the combination model is integrated into the system,and the system is implemented by using related development technologies.