Modeling a nonlinear process using the exponential autoregressive time series model

Huan Xu, Feng Ding, Erfu Yang

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
3 Downloads (Pure)

Abstract

The parameter estimation methods for the nonlinear exponential autoregressive (ExpAR) model are investigated in this work. Combining the hierarchical identification principle with the negative gradient search, we derive a hierarchical stochastic gradient algorithm. Inspired by the multi-innovation identification theory, we develop a hierarchical-based multi-innovation identification algorithm for the ExpAR model. Introducing two forgetting factors, a variant of the hierarchical-based multi-innovation identification algorithm is proposed. Moreover, to compare and demonstrate the serviceability of these algorithms, a nonlinear ExpAR process is taken as an example in the simulation.

Original languageEnglish
Pages (from-to)2079-2092
Number of pages14
JournalNonlinear Dynamics
Volume95
Issue number3
Early online date6 Dec 2018
DOIs
Publication statusPublished - 28 Feb 2019

Keywords

  • hierarchical identification
  • multi-innovation identification
  • negative gradient search
  • nonlinear ExpAR model
  • parameter estimation

Cite this