神经网络在石油价格预测中的仿真研究

Research on Petroleum Price Prediction Based on Neural Network

在石油价格预测问题的研究中,石油价格预测与供求和经济政治有关。针对石油价格变化具有高度非线性、噪声和价格因子难以确定等特点,传统预测方法都是根据线性变化规律提出的,因此对石油价格预测精度低。为提高石油价格预测精度,提出一种主成分分析的BP神经网络石油价格预测模型。可通过定性分析获得石油价格变化影响因子,然后采用主成分分析消除数据中的噪声并选择重要影响因子,最后重要影响因子作为BP神经网络的输入,采用非线性预测能力强的神经网络对石油价格进行预测。实验结果表明,模型提高了石油价格预测精度,为石油价格预测提供了一种有效方法。

In petroleum price prediction problem isstudied.Oil prices have the features of high nonlinear,noise and the impact factors which are hard to be determined,and the prediction accuracies of traditional forecasting methods based on the linear change rule of oil prices are low.In order to improve the oil price prediction accuracy,the paper puts forward a petroleum price prediction model based on principal component analysis and BP neural network.Firstly,the petroleum price impacting factors are obtained by qualitative analysis,then the principal components analysis is used to choose the important influence factors which are used as the BP neural network＇s inputs;lastly,predicting oil price is predicted by neural network.Experimental results show that the prediction accuracy is improved and it provides an effective,high precision prediction method for petroleum price.