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Forecasting S-shaped diffusion processes via response modelling methodology

Author

Listed:
  • H Shore

    (Ben-Gurion University)

  • D Benson-Karhi

    (Ben-Gurion University)

Abstract

Diffusion processes abound in various areas of corporate activities, such as the time-dependent behaviour of cumulative demand of a new product, or the adoption rate of a technological innovation. In most cases, the proportion of the population that has adopted the new product by time t behaves like an S-shaped curve, which resembles the sigmoid curve typical to many known statistical distribution functions. This analogy has motivated the common use of the latter for forecasting purposes. Recently, a new methodology for empirical modelling has been developed, termed response modelling methodology (RMM). The error distribution of the RMM model has been shown to model well variously shaped distribution functions, and may therefore be adequate to forecast sigmoid-curve processes. In particular, RMM may be applied to forecast S-shaped diffusion processes. In this paper, forty-seven data sets, assembled from published sources by Meade and Islam, are used to compare the accuracy and the stability of RMM-generated forecasts, relative to current commonly applied models. Results show that in most comparisons RMM forecasts outperform those based on any individually selected distributional model.

Suggested Citation

  • H Shore & D Benson-Karhi, 2007. "Forecasting S-shaped diffusion processes via response modelling methodology," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 720-728, June.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:6:d:10.1057_palgrave.jors.2602187
    DOI: 10.1057/palgrave.jors.2602187
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    References listed on IDEAS

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    1. Young, Peg & Ord, J. Keith, 1989. "Model selection and estimation for technological growth curves," International Journal of Forecasting, Elsevier, vol. 5(4), pages 501-513.
    2. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
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    Cited by:

    1. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    2. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    3. Chang, Ching-Ter & Lin, Teng-Chiao, 2009. "Interval goal programming for S-shaped penalty function," European Journal of Operational Research, Elsevier, vol. 199(1), pages 9-20, November.

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