Title :
Forecasting using genetic programming
Author :
Sheta, Alaa F. ; Mahmoud, Ahmed
Author_Institution :
Dept. of Syst. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
Abstract :
In this paper, two models for forecasting the Nile River flow have been developed. The traditional linear autoregressive (AR) model and genetic programming (GP) based model are presented. The performance of both the AR and GP models were tested using a set of measurements recorded at the Donagola station located in the Northern Sudan. A significant improvement of the error when using the GP model for forecasting was achieved
Keywords :
autoregressive processes; forecasting theory; genetic algorithms; rivers; Nile River; Northern Sudan; forecasting; genetic programming; linear autoregressive model; Algorithm design and analysis; Computational modeling; Evolutionary computation; Genetic algorithms; Genetic programming; Predictive models; Rivers; System identification; Systems engineering and theory; Testing;
Conference_Titel :
System Theory, 2001. Proceedings of the 33rd Southeastern Symposium on
Conference_Location :
Athens, OH
Print_ISBN :
0-7803-6661-1
DOI :
10.1109/SSST.2001.918543
