1 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 University of Massachusetts4 Department of Applied Mathematics and Computer Science, Technical University of Denmark5 University of Massachusetts
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.
Quality Engineering, 2009, Vol 21, Issue 3, p. 341-353