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Another Look at Measures of Forecast Accuracy

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Author Info
Rob J. Hyndman ()
Anne B. Koehler

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Abstract

We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition and the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be inadequate, and many of them are degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.

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File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2005/wp13-05.pdf
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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 13/05.

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Length: 18 pages
Date of creation: May 2005
Date of revision:
Handle: RePEc:msh:ebswps:2005-13

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Related research
Keywords: Forecast accuracy; Forecast evaluation; Forecast error measures; M-competition; Mean absolute scaled error.;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Technology Assessment
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454. [Downloadable!] (restricted)
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  2. Koning, Alex J. & Franses, Philip Hans & Hibon, Michele & Stekler, H.O., 2005. "The M3 competition: Statistical tests of the results," International Journal of Forecasting, Elsevier, vol. 21(3), pages 397-409. [Downloadable!] (restricted)
  3. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October. [Downloadable!] (restricted)
  4. Chatfield, Chris, 1992. "A commentary on error measures," International Journal of Forecasting, Elsevier, vol. 8(1), pages 100-102, June. [Downloadable!] (restricted)
  5. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530. [Downloadable!] (restricted)
  6. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge. [Downloadable!]
  7. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December. [Downloadable!] (restricted)
  8. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.
  9. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June. [Downloadable!] (restricted)
  10. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June. [Downloadable!] (restricted)
  11. Baki Billah & Maxwell L King & Ralph D Snyder & Anne B Koehler, 2005. "Exponential Smoothing Model Selection for Forecasting," Monash Econometrics and Business Statistics Working Papers 6/05, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
    Other versions:
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  3. de Silva, Ashton, 2008. "Forecasting macroeconomic variables using a structural state space model," MPRA Paper 11060, University Library of Munich, Germany. [Downloadable!]
  4. Muhammad Akram & Rob J. Hyndman & J. Keith Ord, 2007. "Non-linear exponential smoothing and positive data," Monash Econometrics and Business Statistics Working Papers 14/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  5. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  6. Antonio García-Ferrer & Ester González-Prieto & Daniel Peña, 2008. "A multivariate generalized independent factor GARCH model with an application to financial stock returns," Statistics and Econometrics Working Papers ws087528, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  7. George Athanasopoulos & Rob J Hyndman & Haiyan Song & Doris C Wu, 2008. "The tourism forecasting competition," Monash Econometrics and Business Statistics Working Papers 10/08, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  8. Pim Ouwehand & Rob J. Hyndman & Ton G. de Kok & Karel H. van Donselaar, 2007. "A state space model for exponential smoothing with group seasonality," Monash Econometrics and Business Statistics Working Papers 7/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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