Design and Application of an International Trade Flow Prediction System Based on Machine Learning Algorithms

In the field of international trade, the role of accurate prediction of trade flows in formulating strategic plans for enterprises and optimizing policy regulation by the government cannot be ignored. This study designs and constructs an international trade flow prediction system. The overall architecture of the system covers five core modules: data collection and preprocessing, feature engineering, model construction, model training and optimization, and application deployment. The data collection and preprocessing module collects historical international trade data and processes the data uniformly. The feature engineering module uses principal component analysis to filter and construct the most representative and predictive feature combination from the data. The model construction module adopts the idea of ensemble learning, integrating the gradient boosting decision tree (GBDT) and the long short-term memory network (LSTM) algorithm. The model training and optimization module uses the Early Stopping method to train the model. The application deployment module encapsulates the prediction model that has been trained and optimized to the mature stage into a convenient and easy-to-use Web application. The system proposed in this paper is superior to the traditional system in terms of prediction accuracy. Its maximum prediction accuracy reaches 99.9%. The average prediction time of the system is 324.475 milliseconds, which is much lower than the 551.05 milliseconds of the ARIMA system. Through case analysis, the application of this system in a multinational manufacturing company has helped the company optimize production planning and supply chain management, reduce operating costs, and enhance market competitiveness.