SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v4i7.4181ArticleForecast of Logistics Demand in the Pearl River Delta Region Based on PCA-GA-SVM Modelhttps://artdesignp.com/journal/SSR/4/7/10.26689/ssr.v4i7.4181YeYiyong2022472022-07-14Regional 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.

Hu P, 2019, Research on Urban Logistics Demand Forediction and Development Countermeasures Based on Arima-BP, Tianjin University of Technology.

Wu M, Li B, 2018, Research on Logistics Industry Development Based on GM (1,1) Grey Prediction Model: Takes the Economic Growth Background in Henan Province as an Example. Henan Science, 36(08): 1305-1312.

Ji Z, 2019, Guizhou Province in Guizhou Guizhou Based on Gray-Markov. Logistics Technology, 42(02): 145-149.

Li L, Yue Y, Tian W, 2019, Evaluation and Prediction of Logistics Capacity in Beijing, Tianjin, and Hebei Based on the Gray Model of PCA and Markov Residues. Journal of Beijing Jiaotong University (Social Science Edition), 18(02): 129-142.

Ma H, Liao Y, 2018, SVR Based on Genetic Algorithm. Logistics Technology, 37(03): 61-64+149.

Cao Z, Yang Z, Liu F, 2018, Regional Logistics Demand Prediction of Support Vector Regression Machine. Journal of Systems Science, 26(04): 79-82+90.

Chen G, 2019, The Cold Chain Logistics Demand Forecast of Henan Fresh Agricultural Products Based on the Grey Model. Modern Trade Industry, 40(11): 56-58.

Liang Y, Yang H, Su H, 2018, Prediction and Analysis of Cold Chain Logistics Demand for Agricultural Products in Tianjin Based on Multiple Linear Regression. Southern Agricultural Machinery, 49(18): 230-231.

Guo M, Li H, 2018, Demand Prediction of Fruits and Vegetables Based on PCA-RBF Neural Network Model. Jiangxi Agricultural Journal, 30(10): 137-141.

Yin Y, Wang D, 2018, Grain Logistics Demand Prediction Analysis in Quanzhou Port Based on BP Neural Network Model. Technology and Industry, 18(11): 82-85.

Tsai W, Huang H, 2019, Combined Model Analysis of Port Logistics Demand Based on BP-RBF Neural Network. Journal of Zhengzhou University (Engineering edition), 40(05): 85-91.

Lu Y, 2018, Forecast of Dalian Port Based on Combined Prediction Model. China Market, 2018(27): 21-24.

Zhao X, Zhang Jun, 2018, Tmall Double 11 Logistics Demand Forecast Based on the Interval Gray Number Prediction Model. Journal of Chongqing Technology and Business University (Natural Science Edition), 35(06): 40-45.