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|Type:||Artigo de periódico|
|Title:||SERFIT - AN ALGORITHM TO FORECAST MINERAL TRENDS|
|Abstract:||A PASCAL computer program, named SERFIT, facilitates the identification of trend model for long-term forecasting and the estimation of model parameters. Model identification is achieved through the computation of slope characteristics from mineral data time series. The trend models generated by the program are: linear, normal, lognormal, and modified exponentials: simple-modified exponential, logistic, derivative logistic, Gompertz, and derivative Gompertz. Parameters of the family of modified exponential models are estimated using Mitscherlich's regression, which is based upon the maximum likelihood method and provides a probability structure for the models. SERFIT is demonstrated on U.S. petroleum production and world copper consumption data.|
MINERAL DEMAND AND PRODUCTION PROJECTIONS
|Editor:||Pergamon-elsevier Science Ltd|
|Appears in Collections:||Artigos e Materiais de Revistas Científicas - Unicamp|
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