@article{(Open Science Index):https://publications.waset.org/pdf/13102,
	  title     = {Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction},
	  author    = {E. Giovanis},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {4},
	  number    = {4},
	  year      = {2010},
	  pages     = {430 - 436},
	  ee        = {https://publications.waset.org/pdf/13102},
	  url   	= {https://publications.waset.org/vol/40},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 40, 2010},
	}