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[期刊论文]

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

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author:

Chen, G.-Y. (Chen, G.-Y..) [1] | Gan, M. (Gan, M..) [2] | Chen, G.-L. (Chen, G.-L..) [3]

Indexed by:

Scopus

Abstract:

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. © 2018 Elsevier Inc.

Keyword:

Generalized exponential autoregressive (GExpAR); Stationary conditions; Time series; Variable projection method

Community:

  • [ 1 ] [Chen, G.-Y.]Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Chen, G.-Y.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
  • [ 3 ] [Gan, M.]Key Laboratory of Intelligent Metro of Univerisites in Fujian, Fuzhou Univeristy, Fuzhou, China
  • [ 4 ] [Gan, M.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
  • [ 5 ] [Gan, M.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 6 ] [Chen, G.-L.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
  • [ 7 ] [Chen, G.-L.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address:

  • [Gan, M.]College of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

Information Sciences

ISSN: 0020-0255

Year: 2018

Volume: 438

Page: 46-57

5 . 5 2 4

JCR@2018

0 . 0 0 0

JCR@2023

ESI HC Threshold:174

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 75

30 Days PV: 0

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