基于BP神经网络模型的通货膨胀预测

The Prediction of Inflation Based on BP Neural Network

通货膨胀的诱因、预测与控制是各国政府、学者乃至普通民众关心的热点问题。运用交叉相关分析法对GDP增长率、M2增长率、工业生产者购进价格指数等因素与通货膨胀间的关系进行了定量分析,确定了各因素与通货膨胀之间的领先、一致和滞后关系;然后结合人工神经网络原理,以先前确定的领先指标作为输入信息建立了基于BP神经网络的通胀预测模型,最后根据模型预测结果,提出了控制通货膨胀的相关政策建议。

The causes of inflation, forecast and control have been an important topic for governments, schol- ars and ordinary people. In this paper, the cross correlation analysis method is used to analysis quantitatively the relationship between GDP growth rate, M2 growth rate, producer price of Industrial Products and inflation factors, to identify whether the relationships are leading, consistent or lag. Based on the artificial neural network theory, this paper establishes a BP neural network model using the previous leading indicators as input information. Finally according to the model prediction results, this paper puts forward related policy suggestions to control inflation.