Gradient radial basis function based varying-coefficient autoregressive model for nonlinear and nonstationary time series

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)809-812
Journal / PublicationIEEE Signal Processing Letters
Volume22
Issue number7
Online published11 Nov 2014
Publication statusPublished - Jul 2015

Abstract

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. © 2014 IEEE.

Research Area(s)

  • Functional-coefficient autoregressive model, gradient radial basis function, nonlinear and nonstationary time series, separable nonlinear least squares