Time series control charts in the presence of model uncertainty

Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Time series control charts are popular methods for statistical process control of autocor-related processes. In order to implement these methods, however, a time series model of the process is required. Since time series models must always be estimated from process data, model estimation errors are unavoidable. In the presence of modeling errors, time series control charts that are designed under the assumption of a perfect model may have an actual in-control average run length that is substantially shorter than desired. This paper presents a method for incorporating model uncertainty information into the design of time series control charts to provide a level of robustness with respect to modeling errors. The focus is on exponentially weighted moving average charts and Shewhart individual charts applied to the time series residuals.

Original languageEnglish (US)
Pages (from-to)891-898
Number of pages8
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Volume124
Issue number4
DOIs
StatePublished - Nov 2002

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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