Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series |
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| Authors: | Min Gan C.L. Philip Chen Long Chen Chun-Yang Zhang |
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| Affiliation: | 1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China;2. Faculty of Science and Technology, University of Macau, Macau, China;3. Faculty of Science and Technology, University of Macau, Macau, China |
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| Abstract: | In this paper, we explore the radial basis function network-based state-dependent autoregressive (RBF-AR) model by modelling and forecasting an ecological time series: the famous Canadian lynx data. The interpretability of the state-dependent coefficients of the RBF-AR model is studied. It is found that the RBF-AR model can account for the phenomena of phase and density dependencies in the Canadian lynx cycle. The post-sample forecasting performance of one-step and two-step ahead predictors of the RBF-AR model is compared with that of other competitive time-series models including various parametric and non-parametric models. The results show the usefulness of the RBF-AR model in this ecological time-series modelling. |
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| Keywords: | forecasting modelling state-dependent model varying coefficient model time series |
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