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.Journal of the Operational Research Society (2007) 58, 720-728. doi:10.1057/palgrave.jors. 2602187 Published online 15 March 2006.
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Acknowledgements—MATHEMATICAs is a registered trademark of Wolfram Research. This research was done with the partial support of the Paul Ivanier Center of Robotics at Ben-Gurion University.
- Diffusion processes
- Inverse normalizing transformations
- Nonlinear regression
- Response modeling methodology
- Sigmoid curves