基于神经网络的多变量时间序列预测及其在股市中的应用

首先分别由开盘价、最低价、最高价和收盘价序列经小波变换得到在大尺度上的各自逼近序列,并由这些逼近序列进行相空间重构,得到各自重构相空间内的点,即矢量列.然后将这4个矢量列组合成一个维数更高的矢量列,作为神经网络的输入,对其进行训练.最后用训练好的网络对2000年初的牛市行情中的上证指数波动趋势进行预测,结果令人满意.

MULTIVARIABLE TIME SERIES PREDICTION BASED ON NEURAL NETWORK AND ITS APPLICATION IN STOCK MARKET

This paper presents a method for predicting multivariable time series with neural networks. First, we implement wavelet decomposition for Shanghai Stock Exchange (SSE) index time series of open, lowest, highest and close prices which are correlated one another and obtain the approximation series at lower resolution, considered as the trend of SSE index time series, by reconstruction with setting coefficients representing details zero. Then four attractors were reconstructed with these delayed approximation series, respectively, thus getting points on attractors in reconstructed phase spaces or four sets of vector series. Then, combined these four vector series as one vector series of higher dimension, which was taken as the inputs to the neural network for training. Finally, the trained network was used to predict trend of SSE index time series at beginning of 2000 when the strongest bull market in history had just started.