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.