Prediction Of Mutton Price Based On PCA_BP Neural Network

Price prediction has played a key role in the stable development of the market economy.It can not only quickly and efficiently pass market information to producers,make adjustments to market fluctuations in a timely manner,but also provide good decision-making basis for decision-makers.The purpose of obtaining greater benefits.Different products have different factors affecting their prices,so exploring effective methods for price prediction is of great significance to the stability and development of market prices.The current price forecasts are mainly concentrated in the fields of oil prices,electricity prices,gold prices,stock markets,real estate and agricultural products.Different commodity objects have different factors that affect prices,and the combined effect of many factors affects the price trend of products.As a product of growth in the natural environment,agricultural and livestock products are easily affected by natural factors.For example,the price of vegetables will be affected by factors such as temperature,precipitation,and sunshine;the price of mutton will be affected by factors such as wind speed,precipitation,and temperature.Fully consider the influencing factors of agricultural and livestock product prices,study the operating mechanism of agricultural and livestock product markets,quantitatively analyze the market price fluctuation law,and find effective methods for price prediction,which is of great significance for the further development and improvement of the agricultural and livestock product market price system.This project mainly takes Sunite sheep as the research object,mainly analyzes the influencing factors affecting the price of mutton,and combines the influencing factors to predict the price of mutton.The research content mainly starts from the following aspects:1)First collect and analyze the factors that affect the price of mutton.In recent years,many factors have affected the price of mutton,mainly including household consumption levels,production,selling prices,prices of other substitutes,and natural conditions(precipitation,wind speed,etc.).Through the correlation test of the impact factors,the more relevant impact factors were screened out;the impact factors were used as input variables,the price of mutton was used as the output variable,and the price prediction of the PCA_BP neural network was performed with the BP without dimensionality reduction processing.The network forms a comparison,and the results show that the model has higher accuracy.2)Linear regression and other methods are used to predict the price of mutton.First,a detailed overview of linear regression,support vector machine regression and other models is given.Secondly,a price prediction regression model is established with the influence factor as the independent variable and the mutton price as the dependent variable.The model has passed the significance test and reflects the actual significance,so it can be used for prediction.