Forecasting of the Nile River Inflows by Genetic Algorithms
The prediction of time series phenomena is a hard and complex task. The selection of a proper statistical model and the setup of its parameters (in terms of the number of coefficients and their values) is also a difficult task and it is usually solved by trial and error. This paper presents a hybrid system that integrates genetic algorithms and traditional statistical models to overcome the model selection and tuning problem. The system is applied to the domain of river Nile inflows forecasting. This domain is characterized by the availability of large amount of data and prediction models. Finally, the results of applying the proposed system are presented and discussed.
KeywordsGenetic Algorithm ARMA Model Akaike Information Criterion Traditional Statistical Model River Water Inflow
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- G.E.P. Box and G.M. Jenkins. Time Series Analysis. Holden-Day, Inc., 1976.Google Scholar
- A.P. Georgakakos and H. Yao. a routing model for the white nile. Technical report, Georgia Institute of Technology, Atlanta, April 1994.Google Scholar
- D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.Google Scholar
- D.C. Montgomery, L.A. Johnson, and J.S. Gardener. Forecasting & Time Series Analysis. John Wiley & Sons, 1992.Google Scholar
- H. Tong. Non-linear Time Series: a Dynamic System Approach. Oxford University Press, 1995.Google Scholar