基于参数优化VMD与XGBoost算法的玉米蛋白粉价格预测

Corn protein powder price forecasting based on parameter optimization VMD and XGBoost algorithm

玉米蛋白粉价格稳定对饲料工业可持续发展和国家粮食安全具有重要意义,但其价格序列具有非平稳、非线性特征,难以精确预测。试验旨在基于XGBoost算法,构建玉米蛋白粉价格预测模型。首先,利用鲸鱼算法(WOA)优化变模分解(VMD)的K值和惩罚参数,对原始价格序列进行自适应分解,降低数据噪声。其次,将Pearson特征筛选后的变量作为极限梯度提升树(XGBoost)模型的输入,进行训练和测试。最后,使用10折交叉验证和学习曲线检验模型性能,并结合SHAP模型分析关键影响因素的非线性效应。结果显示,上一期豆粕期货价格对本期玉米蛋白粉价格波动具有显著的正向影响。研究表明,贝叶斯算法(BO)优化的XGBoost模型具有较好的预测性能,优于基准模型。

The stabilization of corn protein powder price is of great significance to the sustainable development of the feed industry and national food security,but its price series is characterized by non-smooth and non-linear features,which makes it difficult to predict accurately.This study aims to construct a corn protein powder price prediction model based on the XGBoost algorithm.First,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.Second,the Pearson feature-screened variables are used as inputs to the Extreme Gradient Boosting Tree(XGBoost)model for training and testing.Finally,ten-fold cross-validation and learning curves are used to test the model performance and analyze the nonlinear effects of key influencing factors in conjunction with the SHAP model.The study showed that the previous period's soybean meal futures price had a significant positive effect on the current period's corn protein meal price volatility.The results show that the XGBoost model optimized by Bayesian algorithm(BO)has a better predictive performance than the benchmark model.