Genetic evolution of regression models for business and economic forecasting

Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999

Volumn 2, Issue , 1999, Pages 1260-1267

  Kaboudan, M A ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

BUSINESS AND ECONOMICS; DESIRABLE OUTPUTS; ECONOMIC FORECASTING; EVOLUTIONARY METHOD; FITTING OF DATA; GENETIC EVOLUTION; REGRESSION MODEL; SCIENTIFIC RESEARCHES;

ECONOMICS;

EID: 84901402398     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.1999.782587     Document Type: Conference Paper

References (6)

1. nd International Symposium on Information Theory, B. Petrov and F. Caski (eds.). Akademia Kiado, Budapest, pp. 267-810.
2. Angeline, P. J. (1998). Evolving predictors for chaotic time series. In Proceedings of SPIE (Volume 3390): Application and Science of Computational Intelligence, S. Rogers, D. Fogel, J. Bezdek, and B. Bosacchi (eds.). SPIE-The International Society for Optical Engineering, Bellingham, WA. pp. 170-180.
3. Ozaki, T. (1985). Non-linear time series models and dynamical systems. In Hannan, E., Krishnaiah, P. and Rao, M. (editors). Handbook of Statistics 5, Amsterdam: North-Holland.
4. Pindyck, R. and Rubinfeld, D. (1976). Econometric Models and Economic Forecasts. New York: McGraw-Hill, Inc.
5. Singleton, A. (1995). Genetic programming with C++, public domain genetic programming package: http://www.cen. uiuc.edu/~klipp/research/ssprob.cxx. Dublin, NH: Creation Mechanics.
6. Tong, H. (1990). Nonlinear Time Series Analysis: A Dynamical System Approach. Oxford: Oxford University Press.