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Why the damped trend works

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  • E S Gardner

    (University of Houston)

  • E McKenzie

    (University of Strathclyde)

Abstract

The damped trend method of exponential smoothing is a benchmark that has been difficult to beat in empirical studies of forecast accuracy. One explanation for this success is the flexibility of the method, which contains a variety of special cases that are automatically selected during the fitting process. That is, when the method is fitted, the optimal parameters usually define a special case rather than the method itself. For example, in the M3-competition time series, the parameters defined the damped trend method only about 43% of the time using local initial values for the method components. In the remaining series, a special case was selected, ranging from a random walk to a deterministic trend. The most common special case was a new method, simple exponential smoothing with a damped drift term.

Suggested Citation

  • E S Gardner & E McKenzie, 2011. "Why the damped trend works," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1177-1180, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.37
    DOI: 10.1057/jors.2010.37
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    References listed on IDEAS

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    1. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
    2. Flores, Benito E. & Pearce, Stephen L., 2000. "The use of an expert system in the M3 competition," International Journal of Forecasting, Elsevier, vol. 16(4), pages 485-496.
    3. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    4. Armstrong, J. Scott & Fildes, Robert, 2006. "Making progress in forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 433-441.
    5. Hyndman, Rob J. & Billah, Baki, 2003. "Unmasking the Theta method," International Journal of Forecasting, Elsevier, vol. 19(2), pages 287-290.
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    Cited by:

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    4. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    5. Li, Qinyun & Gaalman, Gerard & Disney, Stephen M., 2023. "On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis," International Journal of Production Economics, Elsevier, vol. 265(C).
    6. Gardner, Everette S., 2015. "Conservative forecasting with the damped trend," Journal of Business Research, Elsevier, vol. 68(8), pages 1739-1741.
    7. Costache, Mioara & Sebastian Cristea, Dragos & Petrea, Stefan-Mihai & Neculita, Mihaela & Rahoveanu, Maria Magdalena Turek & Simionov, Ira-Adeline & Mogodan, Alina & Sarpe, Daniela & Rahoveanu, Adrian, 2021. "Integrating aquaponics production systems into the Romanian green procurement network," Land Use Policy, Elsevier, vol. 108(C).
    8. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    9. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    10. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    11. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    12. Rossetti Renato, 2019. "Forecasting the Sales of Console Games for the Italian Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 76-88, September.
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    14. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
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