A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 2, March 2016, Pages: 58-63
Received: Feb. 19, 2016;
Accepted: Feb. 28, 2016;
Published: Mar. 9, 2016
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Samir K. Safi, Dept. of Economics and Statistics, Faculty of Commerce, the Islamic University of Gaza, Gaza, Palestine
Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.
Samir K. Safi,
A Comparison of Artificial Neural Network and Time Series Models for Forecasting GDP in Palestine, American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 2,
2016, pp. 58-63.
Bahramianfar, P. Forecasting US Home Prices with Neural Network and Fuzzy Methods. LAP LAMBERT Academic Publishing, Germany, 2015.
Blanco, A., Pino-Mejías, R., Lara, J., Rayo, S. Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications, 2013, 40(1): 356-364.
Box, G. E. P. & Jenkins, G. M. Time series analysis, forecasting and control. San Francisco: Holden-Day, 1976.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. Time series analysis forecasting and control, Third Edition. New Jersey: Prentice Hall, 1995.
Cadenas E. & Rivera W. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew Energy, 2009, 34(1): 274-8.
Cryer, J. D. & Chan, K. S. Time series analysis with applications in R, Second Edition. New York: Springer, 2008.
Hamid, S. A., & Habib, A. Financial Forecasting with Neural Networks. Academy of Accounting and Financial Studies Journal. 2014, 8(4).
KÖLMEK, M. A., & Navruz, I. Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turkish Journal of Electrical Engineering & Computer Sciences, 2015, 23(3).
Lee, T. S. & Chen, I. F. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 2005, 28(4): 743–752.
Lee, T. S., Chiu, C. C., Lu, C. J. & Chen, I. F. Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 2002, 23(3): 245–254.
Liu, H., Chen, C., Tian, H., & Li, Y. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable Energy, 2012, 48: 545-556.
Majhi, B., Rout, M., Majhi, R., Panda, G., & Fleming, P. New robust forecasting models for exchange rates prediction. Expert Systems with Applications, 2012, 39: 12658–12670.
Potočnik, P., Strmčnik, E., & Govekar, E. Linear and Neural Network-based Models for Short-Term Heat Load Forecasting. Journal of Mechanical Engineering, 2015, 61(9), 543-550.
Stock, J. and Watson, M. Introduction to Econometrics. Updated third edition. Prentice Hall, 2015.
Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, 2013, 433-441.
Venables, W. N. and Ripley, B. D. Modern Applied Statistics with S. Fourth edition. Springer, 2002.