Prediction of Shrimp Price Based on WOA-VMD-XGBoost Algorithm and SHAP Model

This research aims to construct a WOA-VMD-XGBoost-SHAP model to predict shrimp prices and analyze the nonlinear effects of key predictors. Firstly, the whale algorithm (WOA) is used to optimize the K-value and penalty parameter of the variational mode decomposition (VMD) to adaptively decompose the original price series and reduce the data noise. In addition, the trend, period, high and low frequency, and residual terms obtained from the decomposition of the original price series are used as inputs to the XGBoost model for training and testing. Finally, K-fold cross-validation and learning curves are used to test the model performance and analyze the nonlinear effects of key influencing factors in combination with the SHAP model. The results show that the Bayesian-optimized WOA-VMD-XGBoost model has excellent predictive performance with an R2 of 0.927, which is better than other benchmark models; the fluctuation of shrimp prices is cyclical, and the cyclical term accounts for 67% of the characteristic importance. The model can provide effective technical support and decision-making references for relevant management departments and enterprises to predict the price fluctuation of aquatic products.