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China's cross-border e-commerce (CBEC) is developing rapidly in the last several years and is widely considered as a developing trend of the nation's foreign trade. The prediction of CBEC export volume can effectively reduce the risks, such as goods backlog and long cross-border logistics time, for related enterprises in many ways. However, due to the complex composition of many affecting factors, the prediction accuracy of the most existing methods is usually limited. In the paper, we propose a joint prediction approach that combines the back propagation (BP) neural network model and the support vector machine (SVM) method. A case study with publicly available data in Hangzhou, China, indicates a relative error of less than 1% with the proposed joint predication approach, which is less than the relative error obtained from either BP neural network predication or SVM predication used alone.
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