Chaotic time series forecasting based on dynamic recurrent neural networks model
基于动态递归神经网络模型的混沌时间序列预测

A Dynamic Recurrent Neural Networks(DRNN) model was presented in this paper to forecast chaotic time series.The input dimension of DRNN was decided by minimal embedding dimension.The training samples were generated by means of the stepping recursive phase points.It can improve precision and stability of prediction to use chaotic characteristic to deal with samples and mapping nonlinear function by DRNN.The DRNN model was applied to simulation of Lorenz system and shot-term forecasting of Shanghai stock index.Compared with the traditional standard BP neural network,this proposed model shows higher precision.Therefore,this research proves the effectiveness of the proposed model in the practical prediction of time series.