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The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy

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  • J. Scott Armstrong

Abstract

Using findings from empirical-based comparisons, the author presents nine generalizations that can improve forecast accuracy. These are often ignored by organizations, so that attention to them offers substantial opportunities for gain. Copyright International Institute of Forecasters, 2005

Suggested Citation

  • J. Scott Armstrong, 2005. "The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 29-35, June.
  • Handle: RePEc:for:ijafaa:y:2005:i:1:p:29-35
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    Cited by:

    1. Brighton, Henry & Gigerenzer, Gerd, 2015. "The bias bias," Journal of Business Research, Elsevier, vol. 68(8), pages 1772-1784.
    2. Bera, Soumitra Kumar, 2010. "Forecasting model of small scale industrial sector of West Bengal," MPRA Paper 28144, University Library of Munich, Germany.
    3. Tang, Hui-Wen Vivian & Yin, Mu-Shang, 2012. "Forecasting performance of grey prediction for education expenditure and school enrollment," Economics of Education Review, Elsevier, vol. 31(4), pages 452-462.
    4. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    5. P. J. Lamberson & Scott E. Page, 2012. "Optimal Forecasting Groups," Management Science, INFORMS, vol. 58(4), pages 805-810, April.

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