Title :
An estimation of nonlinear time series with ARCH errors using neural networks
Author :
Asato, Hajime ; Miyagi, Hayao ; Yamashita, Katsumi
Author_Institution :
Ryukyus Univ., Okinawa, Japan
Abstract :
The design of a neural network-based estimation system for nonlinear economic time series, based on the Volterra model, is presented. We use ECLMS (extended correlation least mean squares) algorithms as the noise cancelling method for nonlinear time series. The validity of the proposed method is demonstrated by estimating a high-order Volterra model with ARCH (autoregressive conditional heteroscedasticity) errors. This algorithm has a good performance in solving nonlinear economic time-series estimation problems
Keywords :
Volterra series; correlation methods; economic cybernetics; errors; financial data processing; interference suppression; least mean squares methods; mathematics computing; neural nets; nonlinear estimation; software performance evaluation; time series; ARCH errors; algorithm performance; autoregressive conditional heteroscedasticity; extended correlation least mean squares algorithms; high-order Volterra model; neural networks; noise cancelling method; nonlinear economic time series; nonlinear time series estimation; Econometrics; Economic forecasting; Maximum likelihood estimation; Multi-layer neural network; Neural networks; Noise cancellation; Nonlinear filters; Predictive models; Stochastic processes; Time series analysis;
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
Print_ISBN :
0-7803-6583-6
DOI :
10.1109/ICSMC.2000.884384
