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Using a GMDH-type neural network and ARIMA model to forecasting GDP in Algeria during the period of 1990-2019
Corresponding Author(s) : Abdelkader Sahed
American Journal of Economics and Business Management,
Vol. 3 No. 4 (2020): AJEBM
Abstract
Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.
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- • Napitupulu, T. A. (2010, December). Artificial neural network application in gross domestic product forecasting: an Indonesia case. In 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies (pp. 89-93). IEEE.p89.
- • Wabomba, M. S., Mutwiri, M. P., & Mungai, F. (2016). Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (arima) models. Science Journal of Applied Mathematics and Statistics, 4(2), 64-73.p64.
- • Ahmed, A., & Salan, M. S. A. Forecasting GDP Of Bangladesh Using Time Series Analysis.p7.
- • Shabri, A., & Samsudin, R. (2014). A hybrid GMDH and box-jenkins models in time series forecasting. Applied Mathematical Sciences, 8(62), 3051-3062.p3052.
- • Xie, L., Xiao, J., Hu, Y., Zhao, H., & Xiao, Y. (2017). China’s energy consumption forecasting by GMDH based auto-regressive model. Journal of Systems Science and Complexity, 30(6), 1332-1349.p1333.
- • Carvalho, R. L. D. S., & Delgado, A. R. (2019). Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(5), 324-329.p325.
- • Yang, B., Li, C., Li, M., Pan, K., & Wang, D. (2016, December). Application of ARIMA model in the prediction of the gross domestic product. In 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016). Atlantis Press.
- • Loermann, J., & Maas, B. (2019). Nowcasting US GDP with artificial neural networks.
- • Urrutia, J. D., Longhas, P. R. A., & Mingo, F. L. T. (2019, December). Forecasting the Gross Domestic Product of the Philippines using Bayesian artificial neural network and autoregressive integrated moving average. In AIP Conference Proceedings (Vol. 2192, No. 1, p. 090012). AIP Publishing LLC.
- • Dritsaki, C. (2015). Forecasting real GDP rate through econometric models: an empirical study from Greece. Journal of International Business and Economics, 3(1), 13-19.
- • Giovanis, E. (2010). Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction. World Academy of Science, Engineering and Technology, 64, 638-664.
- • Da Veiga, C. P., Da Veiga, C. R. P., Catapan, A., Tortato, U., & Da Silva, W. V. (2014). Demand forecasting in food retail: A comparison between the Holt-Winters and ARIMA models. WSEAS transactions on business and economics, 11(1), 608-614.p610.
- • Guha, B., & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2).p118.
- • Bakar, N. A., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 237311.p132.
- • Zakria, M., & Muhammad, F. (2009). Forecasting the population of Pakistan using ARIMA models. Pakistan Journal of Agricultural Sciences, 46(3), 214-223.p215.
- • Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP Using ARIMA Models. Reports on Economics and Finance, 5(1), 35-47.p37.
- • Yahya, N. A., Samsudin, R., Shabri, A., & Saeed, F. (2019). Combined group method of data handling models using artificial bee colony algorithm in time series forecasting. Procedia Computer Science, 163, 319-329.p322.
- • Dağ, O. (2015). GMDH-type neural network algorithms for short term forecasting (Master's thesis, MIDDLE EAST TECHNICAL UNIVERSITY).p16.
- • Dag, O., & Yozgatligil, C. (2016). GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms. R J., 8(1), 379.p381.
- • Basheer, H., & Khamis, A. B. (2016). A hybrid group method of data handling (GMDH) with the wavelet decomposition for time series forecasting: A review. ARPN Journal of Engineering and Applied Sciences, 11, 10792-10800.p10792.
References
• Napitupulu, T. A. (2010, December). Artificial neural network application in gross domestic product forecasting: an Indonesia case. In 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies (pp. 89-93). IEEE.p89.
• Wabomba, M. S., Mutwiri, M. P., & Mungai, F. (2016). Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (arima) models. Science Journal of Applied Mathematics and Statistics, 4(2), 64-73.p64.
• Ahmed, A., & Salan, M. S. A. Forecasting GDP Of Bangladesh Using Time Series Analysis.p7.
• Shabri, A., & Samsudin, R. (2014). A hybrid GMDH and box-jenkins models in time series forecasting. Applied Mathematical Sciences, 8(62), 3051-3062.p3052.
• Xie, L., Xiao, J., Hu, Y., Zhao, H., & Xiao, Y. (2017). China’s energy consumption forecasting by GMDH based auto-regressive model. Journal of Systems Science and Complexity, 30(6), 1332-1349.p1333.
• Carvalho, R. L. D. S., & Delgado, A. R. (2019). Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(5), 324-329.p325.
• Yang, B., Li, C., Li, M., Pan, K., & Wang, D. (2016, December). Application of ARIMA model in the prediction of the gross domestic product. In 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016). Atlantis Press.
• Loermann, J., & Maas, B. (2019). Nowcasting US GDP with artificial neural networks.
• Urrutia, J. D., Longhas, P. R. A., & Mingo, F. L. T. (2019, December). Forecasting the Gross Domestic Product of the Philippines using Bayesian artificial neural network and autoregressive integrated moving average. In AIP Conference Proceedings (Vol. 2192, No. 1, p. 090012). AIP Publishing LLC.
• Dritsaki, C. (2015). Forecasting real GDP rate through econometric models: an empirical study from Greece. Journal of International Business and Economics, 3(1), 13-19.
• Giovanis, E. (2010). Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction. World Academy of Science, Engineering and Technology, 64, 638-664.
• Da Veiga, C. P., Da Veiga, C. R. P., Catapan, A., Tortato, U., & Da Silva, W. V. (2014). Demand forecasting in food retail: A comparison between the Holt-Winters and ARIMA models. WSEAS transactions on business and economics, 11(1), 608-614.p610.
• Guha, B., & Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 4(2).p118.
• Bakar, N. A., & Rosbi, S. (2017). Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction. International Journal of Advanced Engineering Research and Science, 4(11), 237311.p132.
• Zakria, M., & Muhammad, F. (2009). Forecasting the population of Pakistan using ARIMA models. Pakistan Journal of Agricultural Sciences, 46(3), 214-223.p215.
• Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP Using ARIMA Models. Reports on Economics and Finance, 5(1), 35-47.p37.
• Yahya, N. A., Samsudin, R., Shabri, A., & Saeed, F. (2019). Combined group method of data handling models using artificial bee colony algorithm in time series forecasting. Procedia Computer Science, 163, 319-329.p322.
• Dağ, O. (2015). GMDH-type neural network algorithms for short term forecasting (Master's thesis, MIDDLE EAST TECHNICAL UNIVERSITY).p16.
• Dag, O., & Yozgatligil, C. (2016). GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms. R J., 8(1), 379.p381.
• Basheer, H., & Khamis, A. B. (2016). A hybrid group method of data handling (GMDH) with the wavelet decomposition for time series forecasting: A review. ARPN Journal of Engineering and Applied Sciences, 11, 10792-10800.p10792.