Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series
Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series
复制标题
DOI：
10.1080/00207721.2014.955552
复制
发表时间：
2016-06
影响因子：
4.3
通讯作者：
Min Gan;C. L. P. Chen;Long Chen;Chun-Yang Zhang
中科院分区：
文献类型：
--
作者：
Min Gan;C. L. P. Chen;Long Chen;Chun-Yang Zhang
在本文中，我们探讨了基于径向基函数网络的状态相关自回归（RBF-AR）模型的建模和预测的生态时间序列：著名的加拿大猞猁数据。研究了RBF-AR模型状态相关系数的可解释性。结果发现，RBF-AR模型可以解释加拿大猞猁循环的相位和密度依赖的现象。将RBF-AR模型的一步和两步提前预测器的样本后预测性能与其他竞争性时间序列模型（包括各种参数和非参数模型）的预测性能进行了比较。结果表明RBF-AR模型在生态时间序列建模中的实用性。
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.