A Simple Mathematical Description of the Impact of Forecast Recovery for AR(2) Processes

John N. Dyer, J. Douglas Barrett

Research output: Contribution to journalArticlepeer-review

Abstract

Autocorrelated data present problems when traditional control charts are applied to the process or are modified to account for the autocorrelation. Forecast-based monitoring schemes have been suggested for use in these cases, but the resulting phenomenon of recovery of forecast errors can have a significant effect on the performance of the control chart chosen. To better enable practitioners to select a control chart for forecast based schemes, this article describes the dynamic mathematical behavior of one-step-ahead forecast errors for various AR (2). This behavior is used to explain the varying performance results of the Individuals, EWMA, and CES control charts applied to the one-step-ahead forecast errors. The article provides an introduction to autocorrelated processes, a description of various models and process shifts, a discussion of the impact of forecast recovery for the AR(2) model, realizations of the AR(2) process in regards to recovery rates and model parameters, a discussion of control chart performance, and conclusions.
Original languageAmerican English
JournalPalmetto Review
Volume3
StatePublished - 2000

DC Disciplines

  • Applied Statistics
  • Statistical Methodology
  • Statistical Models

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