Title :
Time series prediction by a modular structured neural network
Author_Institution :
Dept. of Inf. Process. Eng., Fukuyama Univ., Japan
Abstract :
This paper proposes a prediction method for nonstationary time series data with time varying parameters. First a modular structured neural network is newly introduced for the purpose of modeling the changing properties of time varying parameters. This neural network is constructed by the hierarchical combination of neural networks NNT for time series data prediction and NNW for weight prediction. Next is proposed a method to determine the length of the local stationary section by using the additive learning ability of multilayered neural networks. Finally the validity and effectiveness of the proposed method are confirmed through simulation experiments
Keywords :
forecasting theory; learning (artificial intelligence); multilayer perceptrons; prediction theory; time series; time-varying systems; NNT; NNW; modular structured neural network; multilayered neural networks; nonstationary time series data; time series prediction; time varying parameters; weight prediction; Additives; Computational complexity; Data engineering; Information processing; Multi-layer neural network; Neural networks; Prediction methods; Predictive models; Recurrent neural networks;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687255
