Research on Interval Prediction of Nonlinear Chaotic Time Series Based on New Neural Networks
Weijin Jiang, Pu Wang · 2006
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayesian regularization. Furthermore the model is applied to forecast the import and export trades in an industry. The results show that the improved TDNN model has excellent generalization capabilities, which can not only learn the historical curve, but efficiently predict the trend of trade development. In contrast to conventional evaluation of forecasts, we assess the model by calculating the nonlinear characteristics of the predicted and original time series besides analyzing the precision of forecasting. The estimated values demonstrate that the dynamics of the system producing the original series has been reasonably captured in this model