A Study of Neuro-Fuzzy Inference System for Gross Domestic Product Growth Forecasting
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A Study of Neuro-Fuzzy Inference System for Gross Domestic Product Growth Forecasting

Authors: Ε. Giovanis

Abstract:

In this paper we present a Adaptive Neuro-Fuzzy System (ANFIS) with inputs the lagged dependent variable for the prediction of Gross domestic Product growth rate in six countries. We compare the results with those of Autoregressive (AR) model. We conclude that the forecasting performance of neuro-fuzzy-system in the out-of-sample period is much more superior and can be a very useful alternative tool used by the national statistical services and the banking and finance industry.

Keywords: Autoregressive model, Forecasting, Gross DomesticProduct, Neuro-Fuzzy

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084618

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[1] Atsalakis, S.G. and G.I. Atsalakis, "Fruit production forecasting by neuro-fuzzy techniques," Paper prepared for presentation at the 113th EAAE Seminar "A resilient European food industry and food chain in a challenging world", Chania, Crete, Greece, September, pp. 3-6, 2009
[2] Ballini, R., I. Luna, L.M. Lima and R.L.F. da Silveira, "A comparative analysis of neurofuzzy, ANN and ARIMA models for Brazilian stock index forecasting," SCE - Computing in Economics and Finance, 1995
[3] D. Mar─ìek, "Stock Price Forecasting: Autoregressive Modelling and Fuzzy Neural Network," Proceedings of the 1999 EUSFLAT-ESTYLF Joint Conference, Palma de Mallorca, Spain, September, pp. 22-25, 1995
[4] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New Jersey, 2008, pp. 560-579
[5] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference Systems," IEEE Trans. on Systems, Man, and Cybernetics, vol. 23, No. 3, pp. 665-685, 1993
[6] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control," Proceedings of the IEEE, vol. 83, no. 3, 378-406, March, 1995
[7] E.H. Moore, "On the reciprocal of the general algebraic matrix," Bulletin of the American Mathematical Society, vol. 26, pp. 394-395, 1920
[8] R. Penrose, "A generalized inverse for matrices," Proceedings of the Cambridge Philosophical Society, vol. 51, pp. 406-413, 1955
[9] M. Petrou, and P. Bosdogianni, Image Processing: The Fundamentals, John Wile & Sons, 2000
[10] L. Khan, S. Anjum and R. Bada, "Standard Fuzzy Model Identification using Gradient Methods," World Applied Sciences Journal, vol. 8, no. 1, pp. 01-09, 2010
[11] M.A. Denai, F. Palis and A. Zeghbib, ANFIS Based Modelling and Control of Non-Linear systems: A Tutorial, IEEE Conf. on Systems, Man, and Cybernetics, vol. 4, pp. 3433-3438, 2004
[12] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470