山东农业大学学报（自然科学版）2024，Vol.55Issue(3)：367-375,9.DOI:10.3969/j.issn.1000-2324.2024.03.008
基于STL-Informer-ARIMA组合模型的猪肉价格预测方法研究
Pork Price Prediction Method Based on STL-Informer-ARIMA Combined Model
摘要
Abstract
Reasonable pork prices forecasting is significant for stabilizing the fluctuation of pork market prices and promoting the healthy and sustainable development of the pig industry.We conducted an in-depth study of the influencing factors of pork prices and integrated 29 related price data.Based on the analysis of the data characteristics and the limitation of the Informer model in pork price data extraction,it is improved by replacing the self-attention mechanism ProbAttention with the Synthesizer model's mechanism,and the price fluctuation module is introduced.On this basis,we propose a new combined price forecasting model STL-Informer-ARIMA,which combines Random Forest and Recursive Feature Elimination for feature selection.The model utilizes the Seasonal and Trend Decomposition Using Loess method to decompose the pork(lean hogs)prices,employs the ARIMA model to predict the seasonal component,and adopts the improved Informer model to predict the trend and residual components.Experimental results show that the MSE of the STL-Informer-ARIMA combined model is 0.532,MAE is 0.446,RMSE is 0.729,MAPE is 0.030,and R² is 0.958.Compared with commonly used price forecasting models such as LSTM,SVR,and GRU,the combination model has improved the accuracy and reliability of pork price prediction.关键词
猪肉/价格/特征选择/改进的Informer/组合模型Key words
Pork/prices/feature selection/improved Informer/combinatorial modeling分类
管理科学引用本文复制引用
王杰,董国奥,李俊清..基于STL-Informer-ARIMA组合模型的猪肉价格预测方法研究[J].山东农业大学学报（自然科学版）,2024,55(3):367-375,9.基金项目
国家重点研发计划政府间/港澳台重点专项:基于数字信息技术的中欧食品安全过程控制体系的建与示范(2019YFE010380) （2019YFE010380）
山东省科技型中小企业创新能力提升工程项目:设施番茄产业链智慧化管理与决策关键技术研 （）
