@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}, }