Research on Consumption Prediction of Daily Necessities Based on Combination Model and Its Influence on Upgrading of Industrial Structure

Abstract

The development of big data and artificial intelligence ensures the industrial innovation of traditional commodities. Economic transformation and upgrading has become the general trend of social development. Based on the public data of the Rossmann store, a well-known German chain store, and the consumption prediction model of daily necessities based on random forest and GBRT is proposed in this paper. By combining the model to initialize residuals, not only the training speed of the model is effectively improved but also more accurate prediction is obtained. Based on the analysis of the big data, this paper puts forward the influence of daily necessities consumption forecast on the upgrading of industrial structure and constructs a value creation and transmission system for daily necessities, so as to promote the rationalization level of the consumer industrial structure and provide a reference for the upgrading of the same type of commodity industrial structure.