Application of Adaptive Network-Based Fuzzy Inference System in Macroeconomic Variables Forecasting
In this paper we apply an Adaptive Network-Based
Fuzzy Inference System (ANFIS) with one input, the dependent
variable with one lag, for the forecasting of four macroeconomic
variables of US economy, the Gross Domestic Product, the inflation
rate, six monthly treasury bills interest rates and unemployment rate.
We compare the forecasting performance of ANFIS with those of the
widely used linear autoregressive and nonlinear smoothing transition
autoregressive (STAR) models. The results are greatly in favour of
ANFIS indicating that is an effective tool for macroeconomic
forecasting used in academic research and in research and application
by the governmental and other institutions
[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] 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
[5] 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
[6] M. S. Bartlett, "Periodogram analysis and continuous spectra,"
Biometrika, vol. 37, pp. 1-16, 1950
[7] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New
Jersey, 2008, pp. 560-579
[8] K.S. Chan and H. Tong, "On estimating thresholds in autoregressive
models", Journal of Time Series Analysis, vol. 7, pp. 178-190, 1986
[9] T. Teräsvirta and H.M. Anderson, "Characterizing nonlinearities in
business cycles using smooth transition autoregressive models", Journal
of Applied Econometrics, vol. 7, pp. 119-136, 1992
[10] T. Teräsvirta, C.F. Lin and C.W.J. Granger "Power of the Neural
Network Linearity Test", Journal of Time Series Analysis, vol. 14, pp.
209-220, 1993
[11] T. Teräsvirta, "Specification, Estimation, and Evaluation of Smooth
Transition Autoregressive Models", Journal of the American Statistical
Association, vol. 89, no. 425, 208-218, 1994
[12] 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
[13] 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
[14] E.H. Moore, "On the reciprocal of the general algebraic matrix," Bulletin
of the American Mathematical Society, vol. 26, pp. 394-395, 1920
[15] R. Penrose, "A generalized inverse for matrices," Proceedings of the
Cambridge Philosophical Society, vol. 51, pp. 406-413, 1955
[16] M. Petrou, and P. Bosdogianni, Image Processing: The Fundamentals,
John Wile & Sons, 2000
[17] 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
[18] 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
[19] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470
[20] J. G. MacKinnon, "Numerical Distribution Functions for Unit Root and
Cointegration Tests", Journal of Applied Econometrics, vol. 11, pp. 601-
618, 1996
[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] 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
[5] 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
[6] M. S. Bartlett, "Periodogram analysis and continuous spectra,"
Biometrika, vol. 37, pp. 1-16, 1950
[7] W.H Greene, Econometric Analysis, Sixth Edition, Prentice Hall, New
Jersey, 2008, pp. 560-579
[8] K.S. Chan and H. Tong, "On estimating thresholds in autoregressive
models", Journal of Time Series Analysis, vol. 7, pp. 178-190, 1986
[9] T. Teräsvirta and H.M. Anderson, "Characterizing nonlinearities in
business cycles using smooth transition autoregressive models", Journal
of Applied Econometrics, vol. 7, pp. 119-136, 1992
[10] T. Teräsvirta, C.F. Lin and C.W.J. Granger "Power of the Neural
Network Linearity Test", Journal of Time Series Analysis, vol. 14, pp.
209-220, 1993
[11] T. Teräsvirta, "Specification, Estimation, and Evaluation of Smooth
Transition Autoregressive Models", Journal of the American Statistical
Association, vol. 89, no. 425, 208-218, 1994
[12] 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
[13] 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
[14] E.H. Moore, "On the reciprocal of the general algebraic matrix," Bulletin
of the American Mathematical Society, vol. 26, pp. 394-395, 1920
[15] R. Penrose, "A generalized inverse for matrices," Proceedings of the
Cambridge Philosophical Society, vol. 51, pp. 406-413, 1955
[16] M. Petrou, and P. Bosdogianni, Image Processing: The Fundamentals,
John Wile & Sons, 2000
[17] 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
[18] 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
[19] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470
[20] J. G. MacKinnon, "Numerical Distribution Functions for Unit Root and
Cointegration Tests", Journal of Applied Econometrics, vol. 11, pp. 601-
618, 1996
@article{"International Journal of Business, Human and Social Sciences:62251", author = "Ε. Giovanis", title = "Application of Adaptive Network-Based Fuzzy Inference System in Macroeconomic Variables Forecasting", abstract = "In this paper we apply an Adaptive Network-Based
Fuzzy Inference System (ANFIS) with one input, the dependent
variable with one lag, for the forecasting of four macroeconomic
variables of US economy, the Gross Domestic Product, the inflation
rate, six monthly treasury bills interest rates and unemployment rate.
We compare the forecasting performance of ANFIS with those of the
widely used linear autoregressive and nonlinear smoothing transition
autoregressive (STAR) models. The results are greatly in favour of
ANFIS indicating that is an effective tool for macroeconomic
forecasting used in academic research and in research and application
by the governmental and other institutions", keywords = "Linear models, Macroeconomics, Neuro-Fuzzy,Non-Linear models", volume = "4", number = "4", pages = "441-8", }
