A Novel Method for Nonlinear Time Series Forecasting of Time-Delay Neural Network

作     者：JIANG Weijin XU Yuhui 

作者机构：Department of Computer Hunan University of TechnologyZhuzhou 412008 Hunan China Department ot Information and Computer Hunan University ofTechnology Zhuzhou 412008 Hunan China 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2006年第11卷第5期

页      面：1357-1361页

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported bythe Natural Science Foundation of Hunan Province(2001ABB006  2003ABA043) 

主　　题：nonlinear prediction phase space reconstruction BP Bayesian regularization 

摘      要：Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.