引入小波分解的神经网络预测——以玉米大宗商品价格序列为例

Neural Network Prediction with Wavelet Decomposition:A case on maize commodities price sequence

采用世界银行发布的连续752期月度玉米国际价格,将其视为离散价格时间序列,运用小波理论中的Mallat算法,把价格序列分解为若干高频分量和一个低频分量,然后将各分量导进循环神经网络,再累加所得的各个分量预测值,作为最终预测价格。实验表明,引入小波分解的神经网络模型,在玉米价格时间序列中,比较灵活地捕捉到高频和低频信号,并准确拟合和预测这些部分的数值,结果表明该方法对于价格频繁剧烈波动的场景具有实用意义。

Dataset of 752 consecutive monthly international prices of maize released by the World Bank,were regarded as discrete price time series,and the wavelet decomposition method based on Mallat’s algorithm was used to split this sequence signal into several high-frequency components and one low-frequency component,each one was then imported into the recurrent neural network,after that the predicted values of each component from the networks were accumulated as the final predicted price.Research experiments show that the neural network model using wavelet decomposition can flexibly capture high-frequency and low-frequency signals in the price time series of maize,and accurately fit and predict the values of those parts,which tells that the method has practical application significance for scenarios with frequent and violent price fluctuations.