Research on pork price prediction based on improved support vector machine

In view of the "hog cycle" phenomenon that frequently occurs in recent years, "high-price and highrisk-to-kill farmers", this paper attempts to use the integrated empirical modality method EEMD method to excavate the "hog cycle" of pork price fluctuations as a predictive length criterion, and introduces The genetic algorithm (GA) is used to optimize the performance parameters such as the penalty parameter C, kernel function g, and loss function p of the support vector machine (SVM) to further optimize the prediction performance of the SVM. The results showed that: Digging through the EEMD method can accurately dig out the "hog cycle" of pork prices; through the comparison of commonly used prediction models, the optimization performance of the support vector machine after optimization by the genetic algorithm is optimal and robust enough. Sex, and more suitable for short "porcine cycle" predictions. The GA-SVM model proposed in this paper helps to guard against the cyclical risks of pork price volatility and is a more scientific price forecasting tool.