Abstract
The prediction of time series phenomena is a hard and complex task. Many statistical models have been used for solving such task. The selection of a proper statistical model and the setup of its parameters (in terms of the number of parameters and their values) are difficult tasks and they are usually solved by trial and error. This paper presents a hybrid system that integrates genetic algorithms -as a search algorithm- and traditional statistical models to overcome the model selection and tuning problems. The system is applied to the domain of river Nile inflows forecasting which is characterized by the availability of large amount of data and prediction models. The model sdeveloped by the proposed system are then compared with other models like traditional statistical methods and ANNs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Box G., E., P., and Jenkins, G., M., (1976). Time Series Analysis: Forecasting and Control. Holden-Day, Inc.
Douglas C. Montgomery, Lynwood A. Johnson, and John S. Gardener, (1992). Forecasting & Time Series Analysis. John Wiley & Sons.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
Georgakakos A., P., and Yao, H., (1994). A Routing Model For The White Nile. Technical Report, April. Georgia Institute of Technology, Atlanta.
Tong, H. (1995). Non-linear Time Series a Dynamic System Approach. Oxford University Press Inc.
Michael, L. P. (1992) “Towards A more Comprehensive Theory of Learning in Computers” Doctoral dissertation, Department of Computer and Information Science, University of California, Santa Cruz.
Michalewicz, Z. (1994) “Genetic Algorithms + Data Structures = Evolution Programs” Springer-Verlag Berlin Heidelberg.
Sales, J. D., Delleur, J. W., Yevjevich, V., and Lane, W. L. (1980) “Applied Modeling of Hydraulic Time Series” Water Resources Publication, Colorado State University.
Zurada, J. M., (1992) “Introduction to Artificial Neural Systems” West Publishing Company.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag
About this paper
Cite this paper
Abdel-Wahab, A.H., El-Telbany, M.E., Shaheen, S.I. (1998). A hybrid GA statistical method for the forecasting problem : The prediction of the river Nile inflows. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_821
Download citation
DOI: https://doi.org/10.1007/3-540-64582-9_821
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64582-5
Online ISBN: 978-3-540-69348-2
eBook Packages: Springer Book Archive