Forecast of Logistics Demand in the Pearl River Delta Region Based on PCA-GA-SVM Model


Regional logistics
Principal component analysis
Support vector regression
Genetic algorithm




Regional economic development is highly correlated with the change of regional logistics. This paper selects the freight volume as the representative index to reflect the development of regional logistics, and constructs the prediction index system of regional logistics demand. Accordingly, the principal component analysis method is used to reduce the data dimension of the prediction index, and the complexity of the prediction model. Further, the support vector regression model is optimized by genetic algorithm which is constructed by using the advantages of support vector machine algorithm in dealing with nonlinear and small sample size problems. The empirical analysis shows that the prediction model based on PCA-GA-SVM has very good prediction accuracy, it can provide valuable reference for regional logistics development and management.


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