农业展望2025，Vol.21Issue(7)：3-10,8.
基于多特征融合的农产品价格预测研究
Agricultural Products Price Prediction Based on Multi-feature Fusion
摘要
Abstract
To address the challenges of non-stationarity,noise interference,and multi-factor coupling in short-term agricultural price prediction,this study proposed a hybrid forecasting model based on multi-feature fusion.Focusing on Chinese cabbage prices in Chongqing,the"Pearson-EMD-RF-SG"framework integrates Pearson correlation analysis,Empirical Mode Decomposition(EMD),and Savitzky-Golay(SG)filter.Key steps include:(1)screening influential factors from 10 competitive crops(e.g.,pumpkin,leek)via Pearson analysis;(2)decomposing price series into intrinsic mode functions(IMFs)and residual trends using EMD;(3)training multi-scale features with Random Forest(RF)and refining predictions via SG smoothing.Experimental results from 130-period data(2022-2024)demonstrate the model's superiority,with RMSE(0.126),MAE(0.082),and R2(0.901)outperforming benchmarks(SVR,XGBoost,MLP).Ablation studies reveal that removing SG or EMD modules increases prediction errors by 21.5%and 34.69%,respectively.The study proved that multi-feature fusion effectively captures nonlinear dynamics in price fluctuations,offering a high-precision solution for short-time series forecasting and supporting decision-making in smart agriculture supply chains.关键词
农产品价格预测/消融实验/多特征融合/随机森林Key words
agricultural products price prediction/ablation experiments/multi-feature fusion/Random Forest引用本文复制引用
Liao Yucheng,Wang Yuanling,Zhao Tianfan..基于多特征融合的农产品价格预测研究[J].农业展望,2025,21(7):3-10,8.基金项目
重庆移通学院校级应用研究项目(KY2024013) （KY2024013）
