Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate timeseries model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods. Disciplines Business | Marketing Comments Suggested Citation: Armstrong, J.S. Evaluating Forecasting Methods. In Principles of Forecasting: A Handbook for Researchers and Practitioners (Ed. J. Scott Armstrong). Kluwer, 2001. Publisher URL: http://www.springer.com/business+%26+management/business+for+professionals/book/ 978-0-7923-7930-0 This book chapter is available at ScholarlyCommons: http://repository.upenn.edu/marketing_papers/146 Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong (ed.): Norwell, MA: Kluwer Academic Publishers, 2001. Evaluating Forecasting Methods J. Scott Armstrong The Wharton School University of Pennsylvania ABSTRACT Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods.