A
GENETIC ALGORITHMS APPROACH TO GROWTH PHASE FORECASTING OF WIRELESS
SUBSCRIBERS
by Rajkumar Venkatesan and V. Kumar
In order to effectively make forecasts in the telecommunications
sector during the growth phase of a new product life cycle, we
evaluate performance of an evolutionary technique: genetic algorithms
(Gas), used in conjunction with a diffusion model of adoption
such as the Bass model. During the growth phase, managers want
to predict (1) future sales per period, (2) the magnitude of sales
during peak, and (3) when the industry would reach maturity. At
present, reliable estimation of parameters of diffusion models
is possible, when sales data includes the peak sales also. Cellular
phone adoption data from estimates obtained from Gas exhibit good
consistency comparable to NLS, OLS, and a naïve time series model
when the entire sales history is considered. When censored datasets
(data points available until the inflection point) are used, the
proposed technique provides better predictions of future sales;
peak sales time period, and peak sales magnitude as compared to
currently available estimation techniques.
International
Journal of Forecasting; Vol. 18 (Year 2002)
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