A novel machine learning approach to predict the export price of seafood products based on competitive information: The case of the export of Vietnamese shrimp to the US market

Abstract

Predicting the export price of shrimp is important for Vietnam’s fisheries. It not only promotes product quality but also helps policy makers determine strategies to develop the national shrimp industry. Competition in global markets is considered to be an important factor, one that significantly influences price. In this study, we predicted trends in the export price of Vietnamese shrimp based on competitive information from six leading exporters (China, India, Indonesia, Thailand, Ecuador, and Chile) who, alongside Vietnam, also export shrimp to the US. The prediction was based on a dataset collected from the US Department of Agriculture (USDA), the Food and Agriculture Organization of the United Nations (FAO), and the World Trade Organization (WTO) (May-1995 to May-2019) that included price, required farming certificates, and disease outbreak data. A super learner technique, which combined 10 single algorithms, was used to make predictions in selected base periods (3, 6, 9, and 12 months). It was found that the super learner obtained results in all base periods that were more accurate and stable than any candidate algorithms. The impacts of variables in the predictive model were interpreted by a SHapley Additive exPlanations (SHAP) analysis to determine their influence on the price of Vietnamese exports. The price of Indian, Thai, and Chinese exports highlighted the advantages of being a World Trade Organization member and the disadvantages of the prevalence of shrimp disease in Vietnam, which has had a significant impact on the Vietnamese shrimp export price.

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

“…This study used decision trees for regression problems. Table 4 indicates the hyperparameters and search ranges of the decision tree model used in this study [ 37 , 38 , 39 ].…”

“…Table 5 depicts the hyperparameters and searching ranges used in the random forecast method. As shown in Table 5 , hyperparameters contain the number of trees in the forest (n_estimators), the max number of levels in each decision tree (max_depth), and the number of data points placed in a node before the node is split (min_samples_split) [ 37 , 38 , 39 , 42 ].…”