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• W2033117307 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." @default.
• W2033117307 created "2016-06-24" @default.
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• W2033117307 date "2015-07-01" @default.
• W2033117307 modified "2023-10-01" @default.
• W2033117307 title "Gradient Radial Basis Function Based Varying-Coefficient Autoregressive Model for Nonlinear and Nonstationary Time Series" @default.
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• W2033117307 doi "https://doi.org/10.1109/lsp.2014.2369415" @default.
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