Title :
Forecasting time series using logical combinations of neural-based networks
Author_Institution :
Fac. of Inf. Technol., King Mongkut´´s Inst. of Technol., Bangkok, Thailand
Abstract :
Forecasts are the basis for planning and decision making. The more accurate the organization´s forecasts, the better prepared it will be to take advantage of future opportunities and to reduce potential risks. Thus, it should come as no surprise that there is a tremendous number of statistical prediction algorithms already in existence. However, most of them are useful only when the time series exhibit little trend or seasonal variations but a great deal of irregular or random variation. Therefore, the objective of this paper is to propose a new intelligent forecasting technique which is constructed by combining n-trained neural-based network together. The experimental results based on simulated data show a significant improvement in forecasting accuracy
Keywords :
decision theory; forecasting theory; neural nets; time series; decision making; intelligent forecasting; neural-based networks; planning; time series; Backpropagation algorithms; Decision making; Econometrics; Economic forecasting; Feedforward neural networks; Fuzzy systems; Genetic algorithms; Intelligent networks; Neural networks; Predictive models;
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
Print_ISBN :
0-7803-6583-6
DOI :
10.1109/ICSMC.2000.886563