Information technology diffusion: a review of empirical research
ICIS '92 Proceedings of the thirteenth international conference on Information systems
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Elements of Forecasting
Operations for learning with graphical models
Journal of Artificial Intelligence Research
A New Approach to Forecasting Container Throughput of Guangzhou Port with Domain Knowledge
International Journal of Knowledge and Systems Science
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Competitive pressures and the speed of technological development have reduced the length of the typical product lifecycle in many markets to a matter of months. In addition to predicting sales of mature products, forecasting effectively in such markets entails addressing the transitional phases of a product's life--the periods after introduction and prior to phase-out. Unfortunately, the sales patterns that emerge in the course of such transitions are not usually amenable to forecasting with conventional methods. This paper describes a forecasting system currently in use at Sun Microsystems, Inc. (a major manufacturer of network computer products) that seeks to address the challenges posed by diminishing product lifecycles. The system combines a diffusion model to describe transitional sales with very general constructs for time series analysis known as dynamic linear models (DLMs). The latter helps represent time-series artifacts such as seasonality and drift which are frequently found in practical forecasting situations, and which are only scantily addressed by diffusion models published to date. The model uses Bayesian statistical techniques, so that it is able to incorporate judgmental information relating to elements of a particular forecasting context, and to use records of actual sales for related products as precedents in forecasting.