Generalized exponential autoregressive models for nonlinear time series: Stationarity, estimation and applications

被引：71
|
作者：
Chen, Guang-yong [1 ,3 ]
Gan, Min [2 ,3 ,4 ]
Chen, Guo-long [3 ,4 ]
机构：
[1] Fuzhou Univ, Ctr Discrete Math & Theoret Comp Sci, Fuzhou, Fujian, Peoples R China
[2] Fuzhou Univ, Key Lab Intelligent Metro Univ Fujian, Fuzhou, Fujian, Peoples R China
[3] Fuzhou Univ, Fujian Prov Key Lab Network Comp & Intelligent In, Fuzhou, Fujian, Peoples R China
[4] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China
来源：
INFORMATION SCIENCES | 2018年 / 438卷
基金：
中国国家自然科学基金;
关键词：
Generalized exponential autoregressive (GExpAR); Stationary conditions; Variable projection method; Time series; NEURAL-NETWORK; LEAST-SQUARES; ERGODICITY; SELECTION;
D O I：
10.1016/j.ins.2018.01.029
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The generalized exponential autoregressive (GExpAR) models are extensions of the classic exponential autoregressive (ExpAR) model with much more flexibility. In this paper, we first review some development of the ExpAR models, and then discuss the stationary conditions of the GExpAR model. A new estimation algorithm based on the variable projection method is proposed for the GExpAR models. Finally, the models are applied to two real world time series modeling and prediction. Comparison results show that (i) the proposed estimation approach is much more efficient than the classic method, (ii) the GExpAR models are more powerful in modeling the nonlinear time series. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码：46 / 57
页数：12
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