联合谐波小波与递归神经网络的股市时间序列预测

为了提高神经网络对股市时间序列的预测精度，首先利用谐波小波对股票市场数据进行多尺度分解，将其分解为不同尺度且具有平移不变特征的谐波小波分量；然后根据股市时间序列的特点，构建递归神经网络模型进行短期预测，以不同尺度的谐波小波分量为输入数据，对股市数据进行多尺度预测；最后对不同尺度的预测结果进行谐波小波重构，得到最终的股市预测数据。对我国股票市场进行了实验分析，结果表明：股市时间序列经谐波小波分解后，股市数据中不同投资时间水平的价格波动可以被较好的分离，有效地提高了股票市场数据的预测精度。

Forecast of China Stock Time Series Combining Harmonic Wavelet and Neural Network

For improving the prediction accuracy of the stock market datas by neural network, the stock market data is decomposed into a series wavelet components by harmonic wavelet, which are shift invariant and have different scale. Then, constructing the recursion neural network based on the charaters of the stock market, and each harmonic wavelet componet of stock market data is predicted by using of the constructed neural network. At last, the final stock market forecast data is obtained by harmonic wavelet reconstruction for forecasting results of different scale wavelet componets. The experimental results shows that the price fluctuations of different investmetn time horizon in stock market data can be well seperated, after the stock time series is decomposed by harmonic wavelet, the predicting accuracy of stock market is improved efficiently.