A Prediction Method of Non-Stationary Time Series Data by Using a Modular Structured Neural Network (Special Section on Signal Processing for Nonstationary Processes Based on Modelling)

概要

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This paper proposes a prediction method for non-stationary time series data with time varying parameters. A modular structured type neural network is newly introduced for the purpose of grasping the changing property of time varying parameters. This modular structured neural network is constructed by the hierarchical combination of each neural network (NNT: Neural Network for Prediction of Time Series Data) and a neural network (NNW: Neural Network for Prediction of Weights). Next, we propose a reasonable method for determination of the length of the local stationary section by using the additive learning ability of neural networks. Finally, the validity and effectiveness of the proposed method are confirmed through simulation and actual experiments.
社団法人電子情報通信学会の論文
1997-06-25