Regional Logistics Demand Forecast Based on Least Square and Radial Basis Function

• Published in: 东华大学学报（英文版） Vol. 37; no. 5; pp. 446 - 454
• Main Authors: WEI Leqin, ZHANG Anguo
• Format: Journal Article
• Language: English
• Published: School of Humanities and Teachers' Education, Wuyi University, Wuyishan 354300, China%College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China 31.10.2020
• Subjects: demand forecast; least square and radial basis function( LS-RBF); regional logistics
• DOI: 10.19884/j.1672-5220.202008057
• ISSN: 1672-5220

U491; Regional logistics demand forecast is the basis for government departments to make logistics planning and logistics related policies. It has the characteristics of a small amount of data and being nonlinear, so the traditional prediction method can not guarantee the accuracy of prediction. Taking Xiamen City as an example, this paper selects the primary industry, the secondary industry, the tertiary industry, the total amount of investment in fixed assets, total import and export volume, per capita consumption expenditure, and the total retail sales of social consumer goods as the influencing factors, and uses a combining model least square and radial basis function ( LS-RBF) neural network to analyze the related data from years 2000 to 2019, so as to predict the logistics demand from years 2020 to 2024. The model can well fit the training data, and the experimental results obtained from the comparison between the predicted value and the actual value in 2019 show that the error rate is very small. Therefore, the prediction results are reasonable and reliable. This method has high prediction accuracy, and it is suitable for irregular regional logistics demand forecast.