Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictiv...Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.展开更多
文摘Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.
