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)
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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
Issue number3
Early online date6 Dec 2018
Publication statusPublished - 28 Feb 2019


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

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