Artículos de revistas
Serfit: An Algorithm To Forecast Mineral Trends
Registro en:
Computers And Geosciences. , v. 21, n. 5, p. 703 - 713, 1995.
983004
10.1016/0098-3004(94)00105-4
2-s2.0-0029482640
Autor
Suslick S.B.
Harris D.P.
Allan L.H.E.
Institución
Resumen
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. © 1995. 21 5 703 713 Bewley, Fiebig, A flexible logistic growth model with applications in telecommunications (1988) Intern. Jour. Forecasting, 4 (2), pp. 177-192 (1993) BP Statistical Review, , The British Petroleum Co. p.l.c., BP Educational Service, London Bundgaard-Nielsen, Forecasting in the chemical industry (1972) Ind. Mark. Management, 1 (2), pp. 205-210 Feichtinger, Using growth curves to forecast long-term trends for processed raw materials (1988) Resources Policy, 14 (4), pp. 288-298 Gomes, The use of Mitscherlich's regression law in the analysis of experiments with fertilizers (1953) Biometrics, (9), pp. 498-516 Gomes, Nogueira, Tabelas de polinômios para a interpolação da equação de Mitscherlich (1951) Anais da Escola Superior de Agricultura Luiz de Queiroz, (7), pp. 57-67 Gregg, Hossel, Richardson, Mathematical trend curves: an aid to forecasting (1964) Techniques of Production Control, p. 99. , Monograph No. 1, Oliver and Boyd, London Harris, (1984) Mineral resources appraisal-mineral endowment, resources and potential supply: concepts, methods and cases, p. 445. , Clarendon Press, Oxford Harris, Forecasting of mineral industries (1989) Mineral Economics Program, p. 220. , Lecture Notes MnEc-665, Dept. Mining and Geological Eng. College of Mines. Univ. Arizona Harrison, Pearce, The use of trend curves as an aid to market forecasting (1972) Ind. Mark. Management, 1 (2), pp. 149-170 Hewett, Cycles in metal production (1929) Trans. AIME, (85), pp. 65-93 Hubbert, Degree of advancement of petroleum exploration in United States (1967) Am. Assoc. Petroleum Geologists Bull., 51 (11), pp. 2207-2227 Hubbert, Energy resources (1969) Resources and Man (a study and recommendations by the Committee on Resources and Man, National Academy of Sciences —National Research Council) Chapter 8, p. 157. , W. H. Freeman & Co, San Fransisco Martino, (1990) Technological forecasting for decision making, p. 465. , 3rd ed., McGraw-Hill Book Co, New York Malenbaum, Law of demand for minerals (1975) Proc Council of Economics, 104th Ann. Meet. of Am. Inst. Mining, Metall. Petrol. Eng., pp. 147-155. , February 16–20 Malenbaum, (1978) World demands for raw materials in 1985 and 2000, p. 170. , McGraw-Hill Book Co, New York Meade, The use of growth curves in forecasting market development—A review and appraisal (1984) Journal of Forecasting, 3 (4), pp. 429-451