Abstract
In this paper we explore using the support vector regression (SVR) based on the statistics-learning theory of structural risk minimization for the regional logistics demand. Aiming at the blindness of man made choice of parameter and kernel function of SVR, we apply a chaos optimization method to select parameters of SVR. The proposed approach is used for forecasting logistics demand of Shanghai, The experimental results show that the above method obtained lesser training relative error and testing relative error.
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References
Wang, Y.H.: Logistics Demand Forecast of A Linear Regression Analysis. Shopping Center Modernization 34, 136 (2006) (in Chinese)
Chen, S., Zhou, F.: Based on Gray System Theory Logistics Demand Forecasting Model. Statistics and Decision 3, 59–60 (2006) (in Chinese)
Chu, L.Y., Tian, Z.G., Xie, X.L.: Combination Forecasting Fodel in Logistics Demand Forecasting. Journal of Dalian Maritime University 30(4), 43–46 (2004) (in Chinese)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Deng, N.Y., Tian, Y.J.: A New Method of Data Mining-SVM. Science Press, Beijing (2004) (in Chinese)
Sun, C.J.: Based on K-kernel Function Support Vector Machine. Journal of Huaihai Institute of Technology 12, 1672–1685 (2006) (in Chinese)
Wang, L., Zheng, D.Z., Li, Q.S.: Chaos Optimization Algorithm Research Computing Technology and Automation 20(1), 1–5 (2001) (in Chinese)
May, R.: Simple Mathematical Model with Very Complicated Dynamics. Nature 261, 459–467 (1976)
Hou, R., Zhang, B.X.: MLP Neural Network Based on Regional Logistics Demand Forecasting Method and Its Application. Systems Engineering Theory and Practice 25(12), 43–47 (2005) (in Chinese)
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Yang, H., Zhou, Y., Liu, H. (2010). Chaos Optimization SVR Algorithm with Application in Prediction of Regional Logistics Demand. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_8
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DOI: https://doi.org/10.1007/978-3-642-13498-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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