School of Management, XI'AN University of Finance and Economics, Xi'an, Shaanxi, China.
School of Business, XI'AN University of Finance and Economics, Xi'an, Shaanxi, China.
PLoS One. 2021 Dec 3;16(12):e0260883. doi: 10.1371/journal.pone.0260883. eCollection 2021.
International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.
随着经济全球化的深入，国际贸易日益频繁。为了确保国际贸易和金融的稳定快速发展，通过计算机贸易平台的网络模型提前预测外贸商品的销售趋势尤为重要。为了优化外贸商品销售预测的准确性，在“互联网+外贸”的背景下，以外贸商品销售的可控相关性大数据挖掘、个性化预测机制、智能预测算法、改进的分布式定量和集中定性计算为前提，设计了基于跨境电子商务可控相关性大数据的出口销售动态预测模型(DPMES)。此外，经过相关实验和对比讨论，第一季度到第四季度的预测误差率分别为 2.3%、2.1%、2.4%和 2.4%，也在可接受范围内。实验结果表明，该设计结合了“互联网+”的开放性和可扩展性以及大数据的动态预测，实现了“互联网+外贸”背景下外贸相关数据控制下的商品销售量的智慧、定量和定性预测。该设计的整体性能强于之前的模型，具有更好的动态演变和高实际意义，对国际贸易和金融的发展具有重要意义。
