Title :
A novel nonlinear time series forecasting of time-delay neural network
Author :
Jiang Weijin ; Xu Yuhui ; Cao Dongpo ; Luo Fei
Author_Institution :
Sch. of Comput. & Electron., Hunan Univ. of Commerce, Changsha, China
Abstract :
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 imp&exp 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 forecasting 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 dasiacatchpsila the dynamic characteristic of the nonlinear system which produced the origin serial.
Keywords :
commerce; delays; economic forecasting; neural nets; nonlinear systems; time series; Bayesian regularization; business trend; data combination; forecasting model; nonlinear characteristic value; nonlinear phase space reconstruction prediction; nonlinear system; nonlinear time series forecasting; time delay BP neural network model; Bayesian methods; Chaos; Demand forecasting; Economic forecasting; Macroeconomics; Neural networks; Nonlinear dynamical systems; Power generation economics; Predictive models; Technology forecasting;
Conference_Titel :
Granular Computing, 2009, GRC '09. IEEE International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-4830-2
DOI :
10.1109/GRC.2009.5255115