Accurately predicting the price of exported fishery products is an important task for fisheries because it will enable market trends to be determined, leading to the development of high-quality fishery products. In this study, we predicted prices in selected base periods (2, 3, 6, and 12 months) to investigate how historical data influenced the Vietnamese export price. A dataset (from May 1995 to May 2019) was collected from the US Department of Agriculture (USDA). We initially hypothesized that the dependent variable, Vietnamese export price, was affected by 33 independent variables, but ultimately used 15 key variables, which were chosen on the basis of Akaike information criterion (AIC) to train the models. A tree-based machine learning technique, including the random forest and gradient boosting tree algorithms, was applied for predictions. It was found that the random forest algorithm performed well for historical data for periods of more than 6 months, while the gradient boosting tree algorithm was better over short durations of less than 6 months.

准确预测出口渔业产品的价格是渔业的一项重要任务，因为它将使市场趋势得以确定，从而导致高质量渔业产品的发展。在这项研究中，我们预测了选定基准时段（2、3、6和12个月）的价格，以调查历史数据如何影响越南出口价格。从美国农业部（USDA）收集了一个数据集（从1995年5月到2019年5月）。我们最初假设因变量越南出口价格受33个自变量影响，但最终使用了15个关键变量，这些变量是根据Akaike信息准则（AIC）进行选择来训练模型的。将基于树的机器学习技术（包括随机森林和梯度提升树算法）用于预测。