Research on Price Fluctuations in International Trade Process of Agricultural Products with a Machine Learning Model

In this study, a combination of a gated recurrent unit (GRU) model and a Transformer model was used to predict soybean prices in the agricultural sector. Simulation experiments were conducted. The soybean price prediction algorithm was initially compared with two other algorithms, random forest (RF) and back-propagation neural network (BPNN). Then, ablation experiments were performed on the proposed prediction algorithm. The importance of the feature indicators used in predicting soybean prices was tested. The results indicated that, compared to the RF and BPNN algorithms, the GRU-Transformer model demonstrated a superior performance. Additionally, the results of the ablation experiments revealed that both GRU and Transformer models significantly contributed to the accuracy of soybean price prediction. Moreover, the importance of feature indicators such as soybean imports, soybean exports, soybean oil prices, exchange rates, and soybean meal prices was found to be high.