基于谐波小波与神经网络的钢铁价格预测

应用谐波小波分解算法、混沌分析和神经网络理论提出了一种中国钢铁价格趋势预测的谐波小波神经网络模型。应用广义谐波小波分解算法把原始钢铁价格序列分解到不同的频带上，并在此基础上进一步分析表明，钢铁价格存在混沌特性；再经混沌分析和神经网络进行组合预测，提高了模型对多种目标函数的学习能力，有效改进了预测精度。实验表明，与现有方法相比，该方法具有较高的预测精度。

Prediction of steel price based on harmonic wavelet and neural network

Using harmonic wavelet, chaos analysis and neural network theory, a method is presented to model and forecast steel price. Using harmonic wavelet theory, the steel price time serial is decomposed into different frequency ingredient which can significantly represent potential information of original time serial, and the further analysis of each frequency ingredient indicates that China steel price exists a chaos feature. Then, by using chaos theory and neural network, the forecasting models are established to forecast the every frequency ingredient respectively. By these means, the model can be improved to learn various objective function and more precious prediction can be obtained. The experiments show that the presented method can effectively improve the prediction accuracy.