基于STL-Informer-ARIMA组合模型的猪肉价格预测方法研究OA北大核心CSTPCD

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.