基于分解集成方法的小宗农产品价格预测研究  

Research on Small-scale Agricultural Product Price Prediction Based on Decomposition and Integration Method

作　　者：刘合兵[1] 华梦迪 孔玉杰 席磊[1] 尚俊平[1] LIU Hebing;HUA Mengdi;KONG Yujie;XI Lei;SHANG Junping(College of Information and Management Science,Henan Agricultural University,Zhengzhou 450046,China)

机构地区：[1]河南农业大学信息与管理科学学院,河南郑州450046

出　　处：《运筹与管理》2024年第6期125-131,共7页Operations Research and Management Science

基　　金：河南省研究生教育改革与质量提升工程项目(YJS2023AL046);河南省科技攻关项目(212102110204)。

摘　　要：针对小宗农产品价格序列波动特征中呈现出的非平稳、非线性等问题,提出了一种基于“分解与集成”的WOA-VMD-LSTM组合预测模型。首先利用样本熵作为鲸鱼优化算法(WOA)的适应度函数,对变分模态分解方法(VMD)的两个自由参数进行全局寻优;再使用优化后的变分模态分解方法对价格序列进行分解;最后将得到的多模态分量及残差作为输入特征集成到长短期记忆网络(LSTM)中,构建组合模型。将该方法应用于马铃薯、莲藕、白萝卜、大白菜、西兰花、卷心菜的日均价格数据进行预测,实验结果表明,WOA-VMD-LSTM组合模型的均方根误差分别为0.292,0.381,0.129,0.125,0.782和0.142,且与EMD-LSTM组合模型以及ARIMA模型进行对比,WOA-VMD-LSTM组合模型在多种农产品价格的预测上具有更明显的优势。本研究提出的组合预测模型有助于相关产业对市场进行合理配置。The key deployment of agricultural market work shows the determination of the country to adhere to the stable development of agricultural and rural markets,and also reflects the necessity of studying agricultural product price prediction.The fluctuation range of the price series of smallholder farmers’products is large,and the phenomenon of price sudden increase and decrease occurs frequently,which produces shocking news in society,which is not conducive to the stable development of the agricultural product market.Because the price series of small-scale agricultural products have obvious nonlinear and non-stationary characteristics,the prediction effect of a single model is not good.To this end,this paper proposes a combined prediction model based on“decomposition and integration”.Firstly,the Whale Optimization Algorithm(WOA)is applied to the Variational Modal Decomposition(VMD)algorithm,and the Sample Entropy is used to solve the problem.SampEn is used as the fitness function to screen out the optimal parameters.Then,the optimized variational mode decomposition method is used to realize the multi-mode decomposition of the agricultural product price series,solve the modal confusion problem in the complex small-scale agricultural product series,and obtain the modal components that can reflect the different characteristics of the original series.Secondly,the decomposed components and residual sequences are integrated into the Long Short-Term Memory(LSTM)neural network,which is trained as the feature quantities of the original agricultural product price series,so as to enhance the learning ability of the LSTM neural network and improve the prediction accuracy of the combined model.In this study,the daily average price data of potato,lotus root,white radish,Chinese cabbage,broccoli and cabbage in Henan Province from January 1,2016 to December 31,2021 are selected as the research object,and the combined model method is used to predict the price series of six small-scale agricultural products.The root mean square

关 键 词：变分模态分解 样本熵 鲸鱼优化算法 价格预测 

分 类 号：F201[经济管理—国民经济] O21[理学—概率论与数理统计] TP391[理学—数学]