Abstract
Due to the strong interrelationship of international trade relations, trade factors and market environments are involved in trade transactions between different countries and regions. In the international market, there are frequent trade transactions, large transaction amounts, and uncertainty and complexity. How to effectively predict trade exchanges and related economic activities between my country and other countries or regions is an important issue. For this reason, this article uses association rules algorithm to study its prediction model, the purpose of which is to discover trade risks in time and adjust international trade strategies. This article mainly uses experimental and comparative methods to predict the trade relations between different countries and test the accuracy of the prediction results. Experimental data shows that the accuracy of predicting international trade relations using association rule algorithms can reach more than 80%, and the system is feasible.
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Wang, Y. (2022). Forecast Models of International Trade Relations Based on Association Rules Algorithms. In: Sugumaran, V., Sreedevi, A.G., Xu, Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-031-05237-8_16
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DOI: https://doi.org/10.1007/978-3-031-05237-8_16
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