Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Nonlinear system modeling and robust predictive control based on RBF-ARX model
Engineering Applications of Artificial Intelligence
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Expert Systems with Applications: An International Journal
Information Criteria and Statistical Modeling
Information Criteria and Statistical Modeling
Functional coefficient autoregressive models for vector time series
Computational Statistics & Data Analysis
Penalized spline estimation for functional coefficient regression models
Computational Statistics & Data Analysis
A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling
Information Sciences: an International Journal
An amplitude-dependent autoregressive model based on a radial basis functions expansion
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
A parameter optimization method for radial basis function type models
IEEE Transactions on Neural Networks
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
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Varying-coefficient models have attracted great attention in nonlinear time series analysis recently. In this paper, we consider a semi-parametric functional-coefficient autoregressive model, called the radial basis function network-based state-dependent autoregressive (RBF-AR) model. The stability conditions and existing conditions of limit cycle of the RBF-AR model are discussed. An efficient structured parameter estimation method and the modified multi-fold cross-validation criterion are applied to identify the RBF-AR model. Application of the RBF-AR model to the famous Canadian lynx data is presented. The forecasting capability of the RBF-AR model is compared to those of other competing time series models, which shows that the RBF-AR model is as good as or better than other models for the postsample forecasts.