Forecasting of Export Volume Using Artificial Intelligence Based Algorithms

Yıl 2022, , 715 - 726, 30.06.2022

Erdemalp Özden

https://doi.org/10.17798/bitlisfen.1107311

Öz

Technological breakthroughs have transformed communication and taken transportation, health, and commerce to an unprecedented level. In this way, sudden developments have rapidly affected all countries. In this context, analysis methods are changing compared to the past, and annual analyses fail to catch the trend even for macroeconomic indicators. In this paper, new artificial intelligence-based estimation methods were used to see the future trend of export volume, and their estimation performances were compared by adding them to the classical econometric method. Historical quarterly data from 2013 to 2021 were used in the training and testing phases of the models. For this purpose, the variables of gross domestic product, foreign direct investment, and dollar exchange rate, which affect the export volume, were determined as inputs in estimating the export volume. According to the analysis results, support vector machine model was determined as the best method for predicting export volume in Turkey. This study can provide an essential basis for policymakers to export estimation and formulate their export-enhancing policies effectively.

Anahtar Kelimeler

Export Volume, Forecasting, SVM, ANFIS, MLR

Kaynakça

• [1] Worldbank, “Global Economic Prospects,” Washington, DC, 2022. doi: 10.1596/978-1-4648-1758-8.
• [2] TURKSTAT, “Yıllık Gayrisafi Yurt İçi Hasıla, 2020,” 2021. https://data.tuik.gov.tr/Bulten/Index?p=Yillik-Gayrisafi-Yurt-Ici-Hasila-2020-37184#:~:text=Yıllık verilere dayalı olarak hesaplanan,milyar 883 milyon TL oldu.
• [3] Reuters, “Turkey’s economy grew 11% in 2021; to cool to 3.5% in 2022,” 2022. https://www.reuters.com/markets/asia/turkeys-economy-grew-11-2021-cool-35-2022-2022-02-22/
• [4] D. Guleryuz, “Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models,” Process Saf. Environ. Prot., vol. 149, pp. 927–935, 2021, doi: https://doi.org/10.1016/j.psep.2021.03.032.
• [5] B. Efe, M. Kurt, and Ö. F. Efe, “Hazard analysis using a Bayesian network and linear programming,” Int. J. Occup. Saf. Ergon., vol. 26, no. 3, pp. 573–588, Jul. 2020, doi: 10.1080/10803548.2018.1505805.
• [6] N. Shetewy, A. I. Shahin, A. Omri, and K. Dai, “Impact of financial development and internet use on export growth: New evidence from machine learning models,” Res. Int. Bus. Financ., vol. 61, p. 101643, 2022, doi: https://doi.org/10.1016/j.ribaf.2022.101643.
• [7] C. Qiu, “China’s Economic Forecast Based on Machine Learning and Quantitative Easing,” Comput. Intell. Neurosci., vol. 2022, p. 2404174, 2022, doi: 10.1155/2022/2404174.
• [8] Y. Liu, “Foreign Trade Export Forecast Based on Fuzzy Neural Network,” Complexity, vol. 2021, p. 5523222, 2021, doi: 10.1155/2021/5523222.
• [9] A. Costantiello, L. Laureti, and A. Leogrande, “Estimation and Machine Learning Prediction of Imports of Goods in European Countries in the Period 2010-2019,” vol. 5, pp. 188–205, Jul. 2021.
• [10] H. Jia, R. O. Adland, and Y. Wang, “Global Oil Export Destination Prediction: A Machine Learning Approach,” Energy J., vol. 42, 2021.
• [11] P. Suler, Z. Rowland, and T. Krulicky, “Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China,” Journal of Risk and Financial Management , vol. 14, no. 2. 2021. doi: 10.3390/jrfm14020076.
• [12] N. Minh Khiem, Y. Takahashi, K. Dong, H. Yasuma, and N. Kimura, “Predicting the price of Vietnamese shrimp products exported to the US market using machine learning,” Fish. Sci., vol. 87, Apr. 2021, doi: 10.1007/s12562-021-01498-6.
• [13] A. D. Nugroho and Z. Lakner, “Effect of Globalization on Coffee Exports in Producing Countries : A Dynamic Panel Data Analysis,” vol. 9, no. 4, pp. 419–429, 2022, doi: 10.13106/jafeb.2022.vol9.no4.0419.
• [14] D. Lazarov, “Empirical analysis of export performance and economic growth: the case of Macedonia,” Int. J. Trade Glob. Mark., vol. 12, no. 3–4, pp. 381–393, Jan. 2019, doi: 10.1504/IJTGM.2019.101541.
• [15] I. Mukhlis and L. H. Qodri, “Relationship between Export, Import, Foreign Direct Investment and Economic Growth in Indonesia BT - Proceedings of the Third Padang International Conference On Economics Education, Economics, Business and Management, Accounting and Entrepreneurship (PIC,” Sep. 2019, pp. 729–737. doi: https://doi.org/10.2991/piceeba-19.2019.12.
• [16] M. N. Islam, “Export expansion and economic growth: testing for cointegration and causality,” Appl. Econ., vol. 30, no. 3, pp. 415–425, Mar. 1998, doi: 10.1080/000368498325930.
• [17] A. V Jordaan and J. H. Eita, “Export and Economic Growth in Namibia: A Granger Causality Analysis,” South African J. Econ., vol. 75, no. 3, pp. 540–547, Sep. 2007, doi: 10.1111/j.1813-6982.2007.00132.x.
• [18] S. Abosedra and C. F. Tang, “Are exports a reliable source of economic growth in MENA countries? New evidence from the rolling Granger causality method,” Empir. Econ., vol. 56, no. 3, pp. 831–841, Mar. 2019, doi: 10.1007/s00181-017-1374-7.
• [19] J. Ahmad and A. C. C. Kwan, “Causality between exports and economic growth,” Econ. Lett., vol. 37, no. 3, pp. 243–248, Nov. 1991, doi: 10.1016/0165-1765(91)90218-A.
• [20] S. S. Alhakimi, “Export and Economic Growth in Saudi Arabia: The Granger Causality Test,” Asian J. Econ. Empir. Res., vol. 5, no. 1, pp. 29–35, 2018.
• [21] S. M. R. Jahangir and B. Y. Dural, “Crude oil, natural gas, and economic growth: impact and causality analysis in Caspian Sea region,” Int. J. Manag. Econ., vol. 54, no. 3, pp. 169–184, Sep. 2018, doi: 10.2478/ijme-2018-0019.
• [22] J. S. Mah, “Export expansion, economic growth and causality in China,” Appl. Econ. Lett., vol. 12, no. 2, pp. 105–107, Feb. 2005, doi: 10.1080/1350485042000314343.
• [23] T. O. Awokuse, “Exports, economic growth and causality in Korea,” Appl. Econ. Lett., vol. 12, no. 11, pp. 693–696, Sep. 2005, doi: 10.1080/13504850500188265.
• [24] R. Guntukula, “Exports, imports and economic growth in India: Evidence from cointegration and causality analysis,” Theor. Appl. Econ., vol. 25, no. 2, pp. 221–230, 2018.
• [25] A. S. Kalaitzi and E. Cleeve, “Export-led growth in the UAE: multivariate causality between primary exports, manufactured exports and economic growth,” Eurasian Bus. Rev., vol. 8, no. 3, pp. 341–365, Sep. 2018, doi: 10.1007/s40821-017-0089-1.
• [26] UNCTAD, “World Investment Report,” 2002.
• [27] F. Ahmad, M. U. Draz, and S.-C. Yang, “Causality nexus of exports, FDI and economic growth of the ASEAN5 economies: evidence from panel data analysis,” J. Int. Trade Econ. Dev., vol. 27, no. 6, pp. 685–700, Aug. 2018, doi: 10.1080/09638199.2018.1426035.
• [28] P. Sun, Y. Tan, and G. Yang, “Export, FDI and the welfare gains from trade liberalization,” Econ. Model., vol. 92, pp. 230–238, 2020, doi: https://doi.org/10.1016/j.econmod.2020.01.003.
• [29] K.-L. Wang and C. B. Barrett, “Estimating the Effects of Exchange Rate Volatility on Export Volumes,” J. Agric. Resour. Econ., vol. 32, no. 2, pp. 225–255, Apr. 2007, [Online]. Available: http://www.jstor.org/stable/40987362
• [30] Y. Qian and P. Varangis, “Does exchange rate volatility hinder export growth?,” Empir. Econ., vol. 19, no. 3, pp. 371–396, Sep. 1994, doi: 10.1007/BF01205944.
• [31] P. Das, “Analysis of Collinear Data: Multicollinearity,” in Econometrics in Theory and Practice, Singapore: Springer Singapore, 2019, pp. 137–151. doi: 10.1007/978-981-32-9019-8_5.
• [32] H. Bonakdari, H. Moeeni, I. Ebtehaj, M. Zeynoddin, A. Mahoammadian, and B. Gharabaghi, “New insights into soil temperature time series modeling: linear or nonlinear?,” Theor. Appl. Climatol., vol. 135, no. 3–4, pp. 1157–1177, 2019, doi: 10.1007/s00704-018-2436-2.
• [33] A. Azadeh, S. M. Asadzadeh, and A. Ghanbari, “An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments,” Energy Policy, vol. 38, no. 3, pp. 1529–1536, 2010, doi: 10.1016/j.enpol.2009.11.036.
• [34] D. Guleryuz, “Prediction of Capacity Utilization Rate for Turkey Using Adaptive Neuro-Fuzzy Inference System With Particle Swarm Optimization and Genetic Algorithm,” in Handbook of Research on Advances and Applications of Fuzzy Sets and Logic, 1st ed., S. Broumi and B. M’Sik, Eds. IGI Global, 2022, p. 450. doi: 10.4018/978-1-7998-7979-4.
• [35] D. Guleryuz, “Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS,” J. Artif. Intell. Syst., vol. 3, no. 1, pp. 16–34, Jan. 2021, doi: 10.33969/AIS.2021.31002.
• [36] A. Esfahanipour and W. Aghamiri, “Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis,” Expert Syst. Appl., vol. 37, no. 7, pp. 4742–4748, 2010, doi: 10.1016/j.eswa.2009.11.020.
• [37] M. Blej and M. Azizi, “Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for fuzzy real time scheduling,” vol. 11, pp. 11071–11075, Jan. 2016.
• [38] E. Ozden and D. Guleryuz, “Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital,” Comput. Econ., 2021, doi: 10.1007/s10614-021-10194-7.
• [39] C. Zhang, H. Wei, X. Zhao, T. Liu, and K. Zhang, “A Gaussian process regression based hybrid approach for short-term wind speed prediction,” Energy Convers. Manag., vol. 126, pp. 1084–1092, 2016, doi: 10.1016/j.enconman.2016.08.086.
• [40] N. Chandrasekaran, Radhakhrishna Somanah, Dhirajsing Rughoo, Raj Kumar Dreepaul, Tyagaraja S. Modelly Cunden, and Mangeshkumar Demkah, Digital Transformation from Leveraging Blockchain Technology, Artificial Intelligence, Machine Learning and Deep Learning, vol. 863. Springer Singapore, 2019. doi: 10.1007/978-981-13-3338-5.
• [41] G. Diao, L. Zhao, and Y. Yao, “A dynamic quality control approach by improving dominant factors based on improved principal component analysis,” Int. J. Prod. Res., vol. 53, no. 14, pp. 4287–4303, 2015, doi: 10.1080/00207543.2014.997400.
• [42] W. Chong and C. Pu, “Application of support vector machines in debt to GDP ratio forecasting,” Proc. 2006 Int. Conf. Mach. Learn. Cybern., vol. 2006, no. August, pp. 3412–3415, 2006, doi: 10.1109/ICMLC.2006.258504.
• [43] D. Güleryüz, “Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models .,” Acta Infologica, vol. 5, no. 1, pp. 155–166, 2021.
• [44] R. M. O’Brien, “A Caution Regarding Rules of Thumb for Variance Inflation Factors,” Qual. Quant., vol. 41, no. 5, pp. 673–690, 2007, doi: 10.1007/s11135-006-9018-6.