New Approach of Short-term Stock Prediction Based on Combination of Phase Space Reconstruction Theory and Recurrent Neural Network
基于相空间重构理论与递归神经网络相结合的股票短期预测方法

A new approach of short-term stock prediction using PSRT(Phase Space Reconstruction Theory) combined with RNN(Recurrent Neural Network) was presented according to the complex nonlinear character of stock time series.The optimal delay time and minimal embedding dimension were determined by PSRT and the input dimension of RNN was decided by minimal embedding dimension.The training samples were generated by means of the stepping recursive phase points,which could improve precision and stability of prediction.The new method was applied to shot-term forecasting of Shanghai stock index.Compared to the traditional standard BP neural network,the results showed higher precision.So this research acquires effective progress in the practical prediction of time series.