A Method for Forecasting The Pork Price Based on Fluctuation Forecasting and Attention Mechanism

Abstract

With the continuous development of the economy and improvement of people’s living standards, people’s consumption of meat is getting higher and higher, and China has become the largest pork consumer and producer. The price of pork affects not only the quality of life of the residents but also the development of the pig farming industry to a certain extent. Effective pork price forecasting contributes to social stability and unity, not only to ensure farmers’ income, but also to ensure the relation between supply and demand. This paper synthesizes various indicators related to pork prices in the Chinese pork market, and respectively establishes XGboost, SVM and Random Forest models to make preliminary upward and downward forecasts for the samples. The best forecasting results are used to add price forecasting features, and then the LSTM model optimized by the attention mechanism is used to forecast specific prices. The weekly price data of 201501-202106 from the National Bureau of Statistics used in the experiment compared the forecasting effects of three kinds of price increase and decrease forecasting models and eight kinds of numerical price forecasting models. The results show that the Attention-LSTM method of forecasting pork prices based on up and down forecasts is superior to other methods in pork price forecasting accuracy. RMSE = 1.57, MAE = 1.28, MAPE = 2.83%, all belong to a minimum.