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Improving short-term forecasts

Author

Listed:
  • Sanders, NR
  • Ritzman, LP

Abstract

This empirical study compares the accuracy of combined forecasts, found by averaging individual forecasts from univariate time-series techniques, with judgmental forecasts actually made daily by experienced practitioners in real business settings. The value of judgment is assessed, used alone and in combination with quantitatively derived forecasts. The key finding is that the value of each forecasting approach depends on the characteristics of the time series, namely data variability. Automated quantitative forecasts are superior for time series that are relatively stable. Complete reliance on quantitative procedures is not only more efficient, but reduces forecast errors. However, as the volatility of the time series increases, a point is reached where judgmental inputs are desirable, either to supplement or even to replace the forecasts provided by quantitative techniques.

Suggested Citation

  • Sanders, NR & Ritzman, LP, 1990. "Improving short-term forecasts," Omega, Elsevier, vol. 18(4), pages 365-373.
    • Handle: RePEc:eee:jomega:v:18:y:1990:i:4:p:365-373
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    Citations

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    Cited by:

    1. Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
    2. Yuehjen Shao & Yue-Fa Lin & Soe-Tsyr Yuan, 1999. "Integrated application of time series multiple-interventions analysis and knowledge-based reasoning," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 755-766.
    3. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    4. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
    5. Balakrishnan, Jaydeep & Hung Cheng, Chun, 2009. "The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty," Omega, Elsevier, vol. 37(1), pages 165-177, February.

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