Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series. Issue 8 (10th June 2016)

• Record Type: Journal Article
• Authors: Gan, Min; Philip Chen, C.L.; Chen, Long; Zhang, Chun-Yang
• Is Part Of: International journal of systems science. Volume 47:Issue 8(2016)
• Page Start: 1868
• Page End: 1876
• Publication Date: 2016-06-10
• Subjects: forecasting -- modelling -- state-dependent model -- varying coefficient model -- time series; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2014.955552
• Languages: English
• ISSNs: 0020-7721

Abstract

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.