Research On Regional Logistics Demand Forecasting Of Guangxi

With China-ASEAN Free Trade Area and Guangxi Beibu Gulf Economic Zone established, Guangxi goes into the fast lane of the economic development. The logistics industry as an important part of Guangxi is playing an unprecedented role. In order to formulate a scientific and rational logistics development strategy, effectively integrate logistics resources, and promote the logistics supply and demand balance, we need to forecast regional logistics demand and timely grasp the development trends of the regional market, which could ensure the efficient operation of regional logistics system.This paper studies regional logistics demand forecasting of Guangxi, which is based on the grey theory and BP neural network. Firstly, the paper reviews the research status of regional logistics demand forecasting in detail, and introduces regional logistics demand forecasting theory. Secondly, considering the index system based on the principle of selection, it analyzes the influencing factors of regional logistics demand. Combined with the actual situation of the logistics industry data which are available, it establishes the index system of regional logistics demand forecasting. Thirdly, this paper builds grey forecasting model, BP neural network forecasting model and grey neural network forecasting model. Finally, it is the core of this paper, namely empirical research on regional logistics demand forecasting of Guangxi, which introduces the development situation of regional logistics of Guangxi, uses three prediction models to forecast regional logistics demand of Guangxi, and puts forward the development proposals of regional logistics of Guangxi based on forecast results.The grey forecasting model is suitable for solving the incomplete system with small sample and poor information. The BP neural network can process data with non-linear characteristics very well. Due to the incomplete statistics of regional logistics of Guangxi and non-linear changes of regional logistics demand,this paper constructs a combined forecasting model, which combines the grey theory and BP neural network. Through integrating the advantages of two single forecasting models, the combined forecasting model improves the prediction accuracy of regional logistics demand forecasting of Guangxi. In addition, it also analyzes the relationship between the weight of influencing factors and predictors of regional logistics demand of Guangxi. The relationship between the weight and the results of the combined forecasting model both provide a theoretical basis for the development planning and the strategy formulation of regional logistics of Guangxi.