A Simulation Study and Evaluation of Multivariate Forecast Based Control Charts Applied to ARMA Processes

John N. Dyer, Michael D. Conerly, B. Michael Adams

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Much research had been performed in the area of control charting techniques for monitoring autocorrelated processes, especially regarding forecast based monitoring schemes. Forecast based monitoring schemes involve fitting an appropriate time-series model to the process, generating one step ahead forecast errors, and monitoring the forecast errors with traditional control charts. Another method introduced into the literature involves using multivariate control charts to monitor the ARMA derived one-step-ahead (OSA) and two-step-ahead (TSA) forecast errors. This article provides a broad simulation study and evaluation of the suggested multivariate approaches in regards to various ARMA(1,1) and AR(1) processes, and a comparison to their univariate counterparts.
Original languageAmerican English
JournalJournal of Statistical Computation and Simulation
Volume73
DOIs
StatePublished - 2003

Disciplines

  • Applied Statistics
  • Statistical Methodology

Keywords

  • Autocorrelation
  • Box-Jenkins
  • Statistical Process Control

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