Automatic ARMA identification using neural networks and the extended sample autocorrelation function: a reevaluation

Tim Chenoweth, Robert Hubata, Robert St Louis

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Recently, several researchers have attempted to use neural network approaches in conjunction with the extended sample autocorrelation function (ESACF) to automatically identify ARMA models. The work to date appears promising, but generalizations are limited by the fact that the test and training sets for the neural networks were generated from random perturbations of prototype ESACF tables. This paper develops test and training sets by varying the parameters of actual ARMA processes. The results show that the ability of neural networks to accurately identify the order of an ARMA (p,q) model from its transformed ESACF is much lower than reported by previous researchers, and is especially low for time series with fewer than 100 observations.

Original languageEnglish (US)
Pages (from-to)21-30
Number of pages10
JournalDecision Support Systems
Volume29
Issue number1
DOIs
StatePublished - Jul 2000

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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