Gradient Radial Basis Function Based Varying-Coefficient Autoregressive Model for Nonlinear and Nonstationary Time Series

We propose a gradient radial basis function based varying-coefficient autoregressive (GRBF-AR) model for modeling and predicting time series that exhibit nonlinearity and homogeneous nonstationarity. This GRBF-AR model is a synthesis of the gradient RBF and the functional-coefficient autoregressive...

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Bibliographic Details
Published in:IEEE signal processing letters 2015-07, Vol.22 (7), p.809-812
Main Authors: Min Gan, Chen, C. L. Philip, Han-Xiong Li, Long Chen
Format: Article
Language:English
Subjects:
Adaptation models
Educational institutions
Functional-coefficient autoregressive model
gradient radial basis function
Mathematical models
Networks
nonlinear and nonstationary time series
Nonlinearity
Numerical models
Performance evaluation
Predictive models
Radial basis function
separable nonlinear least squares
Signal processing
Synthesis
Time series
Time series analysis
Vectors
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Summary:We propose a gradient radial basis function based varying-coefficient autoregressive (GRBF-AR) model for modeling and predicting time series that exhibit nonlinearity and homogeneous nonstationarity. This GRBF-AR model is a synthesis of the gradient RBF and the functional-coefficient autoregressive (FAR) model. The gradient RBFs, which react to the gradient of the series, are used to construct varying coefficients of the FAR model. The Mackey-Glass chaotic time series are used to evaluate the performance of the proposed method. It is shown that the GRBF-AR model not only achieves much more parsimonious structure but also much better prediction performance than that of GRBF network.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2369415