Quality Quandaries- Time Series Model Selection and Parsimony

Article

Bisgaard, Soren; Kulahci, Murat   (ASQ; Taylor & Francis)   Eugene M. Isenberg School of Management, University of Massachusetts Amherst & Institute for Business & Industrial Statistics, University of Amsterdam; Department of Informatics and Mathematical Modeling, Technical University of Denmark

Quality Engineering    Vol. 21    No. 3
QICID: 30286    July 2009    pp. 341-353

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Article Abstract

Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy.

Keywords

Modeling; Selection; Time series; ARIMA time series models; Parameters


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