Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction

Authors: E. Giovanis

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.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080502

References:

[1] R.D. Aryal and W. Yao-Wu, "Neural Network Forecasting of the Production Level of Chinese Construction Industry", Journal of Comparative International Management , vol. 29, pp. 319-33, 2003
[2] W. Li, Y. Luo, Q. Zhu, J. Liu and J. Le, "Applications of AR*-GRNN model for financial time series forecasting", Neural Computing and Applications, vol. 17, no. 5/6, pp. 441-448, 2008
[3] N.R Swanson and H. White, "A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks", Review of Economics and Statistics, vol. 79, pp. 540-650, 1997a
[4] N.R Swanson and H. White, "Forecasting economic time series using adaptive versus non-adaptive and linear versus nonlinear econometric models", International Journal of Forecasting, vol. 13, pp. 439-461, 1997b
[5] G. Tkacz, "Neural network forecasting of Canadian GDP growth", International Journal of Forecasting, vol. 17, pp. 57-69, 2001
[6] D. A. Dickey, and W. A. Fuller, "Distribution of the Estimators for Autoregressive Time Series with a Unit Root", Journal of the American Statistical Association. vol. 74, pp. 427-431, 1979
[7] D. Kwiatkowski, P. C. B. Phillips, P. Schmidt and Y. Shin, "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root", Journal of Econometrics, vol. 54, pp. 159-178, 1992
[8] M. S. Bartlett, "Periodogram analysis and continuous spectra," Biometrika, vol. 37, pp. 1-16, 1950
[9] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New Jersey, 2008, pp. 560-579
[10] Haykin, S. (1999), NEURAL NETWORKS: A Comprehensive Foundation, Second Edition Pearson education, Prentice Hall, Delhi, India, 1999, pp. 33-47, 73-76
[11] Z. Michalewicz, Genetic Algorithms + Data Structures= Evolution Programs, Springer, 1995, pp. 33-36
[12] M. Mitchell, An Introduction to Genetic Algorithms, MIT Press Cambridge, Massachusetts, London, England, 1996, pp. 12-13, 58-59, 118
[13] J. G. MacKinnon, "Numerical Distribution Functions for Unit Root and Cointegration Tests", Journal of Applied Econometrics, vol. 11, pp. 601- 618, 1996