Research On Clothing Sales Forecasting Model Based On Grey Correlation Analysis And BP Neural Networ

With the development of economy,garment enterprises are facing a complex and changeable market environment.If they want to be in a favorable position in the competition,they must understand the market demand and grasp the opportunity,which requires an accurate prediction of the future sales of clothing.The traditional clothing sales forecast based on empirical judgment and market research analysis lacks scientific basis,and the factors considered are relatively simple,which can not make the prediction results accurate.Therefore,taking clothing sales as the object,this paper systematically studies the clothing sales combination prediction model.Firstly,this paper combs the research status of clothing sales forecast at home and abroad,and analyzes the problems existing in China’s clothing sales forecast,Then it elaborates the relevant theories and technologies of data prediction,so as to lay a theoretical foundation for the later sales prediction,Then it analyzes the influencing factors of clothing sales forecast,analyzes the product factors and environmental factors affecting clothing sales,and studies the Influencing Indicators of each factor,so as to establish the basis for the construction of clothing sales forecast index system.Finally,the grey correlation analysis and BP neural network are combined to predict clothing sales,the correlation degree of measurement indexes is determined by grey correlation analysis,and the prediction model is established by BP neural network.Through empirical research,it is proved that the prediction result accuracy is high.Comparing the model with statistical prediction method,it is judged that the prediction accuracy of the model is obviously better than statistical method,which proves that the clothing sales prediction based on grey correlation analysis BP neural network is feasible.The conclusions of this paper can be used in practical application,which can provide a reference for enterprises to improve the efficiency and accuracy of garment sales prediction.