Empirical Prediction and Risk Assessment of Chicken Egg Prices in China Using Support Vector Machine Algorithm

摘要

The study was aimed to predict and assess the prices and their corresponding fluctuations of chicken eggs in China with risk warning using Support Vector Machine (SVM) algorithm from the aspects of cost, supply and demand. Through correlation analysis, five crucial influencing factors were chosen in the prediction and the assessment of chicken egg prices. Next, six different SVM models were established and tested with the corresponding optimized parameters and training input datasets collected during 2006-2012 in China. The predicted accuracies of five models was proved to be more than 80% and only one model was 50% by comparison with the actual risk warning values of chicken egg price fluctuations. Specifically, the predicted accuracies of two models were 100%. From the results of these SVM models, it was also inferred that the customer satisfaction index was relatively insignificant, while the cost and demand influencing factors were significant for predicting the prices and their fluctuations of chicken eggs.