How to cite this paper
Alam, T. (2019). Forecasting exports and imports through artificial neural network and autoregressive integrated moving average.Decision Science Letters , 8(3), 249-260.
Refrences
Alavi, S., Mehdinezhad, I & Kahshidinia, B. (2019). A trend study on the impact of social media on advertisement.International Journal of Data and Network Science, 3(3), 185-200.
Ardakani, F. J., & Ardehali, M. M. (2014a). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65, 452-461.
Ardakani, F. J., & Ardehali, M. M. (2014b). Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting. Energy conversion and management, 78, 745-752.
Aydin, G., Jang, H., & Topal, E. (2016). Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers. Energy Sources, Part B: Economics, Planning, and Policy, 11(3), 212-219.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of Econometrics, 18(1), 5-46.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
Deng, J. (2010, August). Energy demand estimation of China using artificial neural network. In Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on (pp. 32-34). IEEE.
Esfahani, H., Tavasoli, K & Jabbarzadeh, A. (2019). Big data and social media: A scientometrics analysis.International Journal of Data and Network Science, 3(3), 145-164.
Feng, Y., Liu, J., & He, Y. (2013). Chaos-based dynamic population firefly algorithm. Journal of Computer Applications, 33(3), 796-799.
Gaida, A. J., Grigorian, T. G., Zarichuk, E. A., & Koshkin, K. V. (2017, May). The decision making mechanisms in sea container traffic management. In Electrical and Computer Engineering (UKRCON), 2017 IEEE First Ukraine Conference on (pp. 935-938). IEEE.
Gilani, E., Salimi, D., Jouyandeh, M., Tavasoli, K & Wong, W. (2019). A trend study on the impact of social media in decision making.International Journal of Data and Network Science, 3(3), 201-222.
Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks, 13(2), 415-425.
Javid, E., Nazari, M & Ghaeli, M. (2019). Social media and e-commerce: A scientometrics analysis. International Journal of Data and Network Science, 3(3), 269-290.
Joachims, T. (1998, April). Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning (pp. 137-142). Springer, Berlin, Heidelberg.
Kankal, M., & Uzlu, E. (2017). Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Computing and Applications, 28(1), 737-747.
Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727.
Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 13(2), 947-958.
Kendall, M. G (1995). Time series. Griffin and Co ltd, London.
Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489.
Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675.
Kotur, D., & Žarković, M. (2016, September). Neural network models for electricity prices and loads short and long-term prediction. In Environment Friendly Energies and Applications (EFEA), 2016 4th International Symposium on (pp. 1-5). IEEE.
Li, A., Liang, S., Wang, A., & Qin, J. (2007). Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering & Remote Sensing, 73(10), 1149-1157.
Liu, D., Niu, D., Wang, H., & Fan, L. (2014). Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy, 62, 592-597.
Liu, B., Fu, C., Bielefield, A., & Liu, Y. Q. (2017). Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network. Energies, 10(10), 1453.
Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
Mollaiy-Berneti, S. (2015). Developing energy forecasting model using hybrid artificial intelligence method. Journal of Central South University, 22(8), 3026-3032.
Montanari, A., Rosso, R., & Taqqu, M. S. (1997). Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. Water Resources Research, 33(5), 1035-1044.
Olajide, J. T., Ayansola, O. A., Odusina, M. T., & Oyenuga, I. F. (2012). Forecasting the Inflation Rate in Nigeria: Box Jenkins Approach. IOSR Journal of Mathematics (IOSR-JM), 3(5), 15-19.
Olgun, M. O., Ozdemir, G., & Aydemir, E. (2012). Forecasting of Turkey's natural gas demand using artifical neural networks and support vector machines. Energy Education Science and Technology Part A: Energy and Research, 30(1), 15-20.
Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2(3), 673-696.
Pedro, H. T., & Coimbra, C. F. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7), 2017-2028.
Pourkhani, A., Abdipour, K., Baher, B & Moslehpour, M. (2019). The impact of social media in business growth and performance: A scientometrics analysis. International Journal of Data and Network Science, 3(3), 223-244.
Salimi, D., Tavasoli, K., Gilani, E., Jouyandeh, M & Sadjadi, S. (2019). The impact of social media on marketing using bibliometrics analysis. International Journal of Data and Network Science, 3(3), 165-184.
Reikard, G. (2009). Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3), 342-349.
Sözen, A., Isikan, O., Menlik, T., & Arcaklioglu, E. (2011). The forecasting of net electricity consumption of the consumer groups in Turkey. Energy Sources, Part B: Economics, Planning, and Policy, 6(1), 20-46.
Sokolov-Mladenović, S., Milovančević, M., Mladenović, I., & Alizamir, M. (2016). Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43-45.
Tayebi, S., Manesh, S., Khalili, M & Sadi-Nezhad, S. (2019). The role of information systems in communication through social media. International Journal of Data and Network Science, 3(3), 245-268.
Tektaş, M. (2010). Weather forecasting using ANFIS and ARIMA models. Environmental Research, Engineering and Management, 51(1), 5-10.
Tsai, F. M., & Huang, L. J. (2017). Using artificial neural networks to predict container flows between the major ports of Asia. International Journal of Production Research, 55(17), 5001-5010.
Uddin, J. (2009). Time series behavior of imports and exports of bangladesh: Evidence from cointegration analysis and error correction model. International Journal of Economics and Finance, 1(2), 156.
Udny Yule, G. (1927). On a method of investigating periodicities in disturbed series, with special reference to Wolfer's sunspot numbers. Philosophical Transactions of the Royal Society of London Series A, 226, 267-298.
Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2013). Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of hydrology, 476, 433-441.
Walker, G. T. (1931). On periodicity in series of related terms. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818), 518-532.
Wang, W. C., Chau, K. W., Xu, D. M., & Chen, X. Y. (2015). Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management, 29(8), 2655-2675.
Wold, H. A. (1938). Study in the analysis of stationary time series, Stockholm. Department of Mathematics State University of New York Stony Brook, NY, 11794.
Zeng, Y. R., Zeng, Y., Choi, B., & Wang, L. (2017). Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy, 127, 381-396.
https://atlas.media.mit.edu/en/profile/country/sau/
http://www.sama.gov.sa/en
Ardakani, F. J., & Ardehali, M. M. (2014a). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65, 452-461.
Ardakani, F. J., & Ardehali, M. M. (2014b). Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting. Energy conversion and management, 78, 745-752.
Aydin, G., Jang, H., & Topal, E. (2016). Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers. Energy Sources, Part B: Economics, Planning, and Policy, 11(3), 212-219.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of Econometrics, 18(1), 5-46.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
Deng, J. (2010, August). Energy demand estimation of China using artificial neural network. In Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on (pp. 32-34). IEEE.
Esfahani, H., Tavasoli, K & Jabbarzadeh, A. (2019). Big data and social media: A scientometrics analysis.International Journal of Data and Network Science, 3(3), 145-164.
Feng, Y., Liu, J., & He, Y. (2013). Chaos-based dynamic population firefly algorithm. Journal of Computer Applications, 33(3), 796-799.
Gaida, A. J., Grigorian, T. G., Zarichuk, E. A., & Koshkin, K. V. (2017, May). The decision making mechanisms in sea container traffic management. In Electrical and Computer Engineering (UKRCON), 2017 IEEE First Ukraine Conference on (pp. 935-938). IEEE.
Gilani, E., Salimi, D., Jouyandeh, M., Tavasoli, K & Wong, W. (2019). A trend study on the impact of social media in decision making.International Journal of Data and Network Science, 3(3), 201-222.
Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks, 13(2), 415-425.
Javid, E., Nazari, M & Ghaeli, M. (2019). Social media and e-commerce: A scientometrics analysis. International Journal of Data and Network Science, 3(3), 269-290.
Joachims, T. (1998, April). Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning (pp. 137-142). Springer, Berlin, Heidelberg.
Kankal, M., & Uzlu, E. (2017). Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Computing and Applications, 28(1), 737-747.
Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727.
Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 13(2), 947-958.
Kendall, M. G (1995). Time series. Griffin and Co ltd, London.
Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489.
Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675.
Kotur, D., & Žarković, M. (2016, September). Neural network models for electricity prices and loads short and long-term prediction. In Environment Friendly Energies and Applications (EFEA), 2016 4th International Symposium on (pp. 1-5). IEEE.
Li, A., Liang, S., Wang, A., & Qin, J. (2007). Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering & Remote Sensing, 73(10), 1149-1157.
Liu, D., Niu, D., Wang, H., & Fan, L. (2014). Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy, 62, 592-597.
Liu, B., Fu, C., Bielefield, A., & Liu, Y. Q. (2017). Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network. Energies, 10(10), 1453.
Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
Mollaiy-Berneti, S. (2015). Developing energy forecasting model using hybrid artificial intelligence method. Journal of Central South University, 22(8), 3026-3032.
Montanari, A., Rosso, R., & Taqqu, M. S. (1997). Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. Water Resources Research, 33(5), 1035-1044.
Olajide, J. T., Ayansola, O. A., Odusina, M. T., & Oyenuga, I. F. (2012). Forecasting the Inflation Rate in Nigeria: Box Jenkins Approach. IOSR Journal of Mathematics (IOSR-JM), 3(5), 15-19.
Olgun, M. O., Ozdemir, G., & Aydemir, E. (2012). Forecasting of Turkey's natural gas demand using artifical neural networks and support vector machines. Energy Education Science and Technology Part A: Energy and Research, 30(1), 15-20.
Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2(3), 673-696.
Pedro, H. T., & Coimbra, C. F. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7), 2017-2028.
Pourkhani, A., Abdipour, K., Baher, B & Moslehpour, M. (2019). The impact of social media in business growth and performance: A scientometrics analysis. International Journal of Data and Network Science, 3(3), 223-244.
Salimi, D., Tavasoli, K., Gilani, E., Jouyandeh, M & Sadjadi, S. (2019). The impact of social media on marketing using bibliometrics analysis. International Journal of Data and Network Science, 3(3), 165-184.
Reikard, G. (2009). Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, 83(3), 342-349.
Sözen, A., Isikan, O., Menlik, T., & Arcaklioglu, E. (2011). The forecasting of net electricity consumption of the consumer groups in Turkey. Energy Sources, Part B: Economics, Planning, and Policy, 6(1), 20-46.
Sokolov-Mladenović, S., Milovančević, M., Mladenović, I., & Alizamir, M. (2016). Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43-45.
Tayebi, S., Manesh, S., Khalili, M & Sadi-Nezhad, S. (2019). The role of information systems in communication through social media. International Journal of Data and Network Science, 3(3), 245-268.
Tektaş, M. (2010). Weather forecasting using ANFIS and ARIMA models. Environmental Research, Engineering and Management, 51(1), 5-10.
Tsai, F. M., & Huang, L. J. (2017). Using artificial neural networks to predict container flows between the major ports of Asia. International Journal of Production Research, 55(17), 5001-5010.
Uddin, J. (2009). Time series behavior of imports and exports of bangladesh: Evidence from cointegration analysis and error correction model. International Journal of Economics and Finance, 1(2), 156.
Udny Yule, G. (1927). On a method of investigating periodicities in disturbed series, with special reference to Wolfer's sunspot numbers. Philosophical Transactions of the Royal Society of London Series A, 226, 267-298.
Valipour, M., Banihabib, M. E., & Behbahani, S. M. R. (2013). Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of hydrology, 476, 433-441.
Walker, G. T. (1931). On periodicity in series of related terms. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818), 518-532.
Wang, W. C., Chau, K. W., Xu, D. M., & Chen, X. Y. (2015). Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resources Management, 29(8), 2655-2675.
Wold, H. A. (1938). Study in the analysis of stationary time series, Stockholm. Department of Mathematics State University of New York Stony Brook, NY, 11794.
Zeng, Y. R., Zeng, Y., Choi, B., & Wang, L. (2017). Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy, 127, 381-396.
https://atlas.media.mit.edu/en/profile/country/sau/
http://www.sama.gov.sa/en