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IMPORT FORECAST OF TURKISH RAW ALUMINUM INDUSTRY WITH MULTILAYER PERCEPTRON (MLP): A REVIEW ON APRIL-DECEMBER 2023 PERIOD

Year 2024, Issue: 68, 57 - 64, 31.08.2024

Kamil Abdullah Eşidir , Yunus Emre Gür

https://doi.org/10.18070/erciyesiibd.1310116

Abstract

This study aims to predict the future import value of Turkey’s unprocessed aluminum sector by using Multilayer Perceptron (MLP), one of the artificial neural network models. The study aims to evaluate the contribution of the unprocessed aluminum sector, which is an important industrial sector of Turkey, to economic growth by focusing on import forecasts for the period April - December 2023. Accurately estimating the amount of imports is of great importance in terms of determining the future strategic planning of the sector and the measures to be taken.
In the study, data provided by “The Turkish Statistical Institute (TURKSTAT)” and “The Central Bank of the Republic of Turkey (CBRT)” were compiled. The results showed that the MLP model is an effective tool in rough aluminum sector import forecasts. Estimates show that Turkey's monthly unprocessed aluminum imports are expected to be between 285 and 322 million dollars for the April-December 2023 period. These forecasts can guide managers and planners who want to determine the future strategic decisions and policies of the industry. It also highlights the success of the MLP model's potential for use in industry and economic forecasting.

Keywords

Artificial Neural Networks MultiLayer Perceptron (MLP) Arima Time Series Unprocessed Aluminum

References

  • Adıgüzel, M. (2022). Dünya’da ve Türkiye’de Alüminyum Sektörü, Dış Ticareti ve Türkiye’nin Rekabet Gücü, Üçüncü Sektör Sosyal Ekonomi Dergisi, 57(4), 2782-2813. doi:10.15659/3.sektor-sosyal-ekonomi.22.11.1821
  • Alobaidi, D. (2022). Evaluation of Automobile Demand Forecast In Turkey Using Artifıcial Neural Networks (Doctoral dissertation). Karabuk University Institute of Graduate Programs Department of Industrial Engineering, Master Thesis, Karabuk.
  • Amiri, A., ve Gerdtham, U. (2011). Relationship between exports, imports, and economic growth in France: evidence from cointegration analysis and Granger causality with using geostatistical models. MPRA Paper No. 34190. Retrieved from http://mpra.ub.uni-muenchen.de/34190/. Erişim Tarihi:25.05.2023.
  • Bakari, S. ve Mabrouki, M. (2017).The nexus between exports, imports, Domestic investment and Economic growth in Japan, Economic Policy, (2116), 0–33. doi.org/10.1227/01.NEU.0000349921.14519.2A
  • Bishop, C. M. (1995), Neural networks for pattern recognition, Oxford University Press, Oxford 482.
  • Campa, J. ve Goldberg, L. S. (1995). Investment in manufacturing, exchange rates and external exposure, Journal of International Economics, 38(3-4), 297-320.
  • Çırakoğlu, Z. (2022), İstanbul Demir ve Demir Dışı Metaller İhracatçıları Birliği (İDDMİB) Alüminyum Sektörü Şubat 2022 Değerlendirmesi, https://turkishmetals.org/storage/files/ihracat_files/1646729168.pdf Erişim Tarihi: 10.05.2023.
  • Demirci, K. M. (2012). Dünya alüminyum ticaretinde Türkiye’nin yeri, Türk Mühendis ve Mimar Odaları Birliği Metalürji Mühendisleri Odası, 17-29.
  • Du, K-L ve Swamy, M. N. S. (2014), Neuronal networks and statistical learning, Springer, Berlin.
  • Dudin, M. N., Voykova, N. A., Frolova, E. E., Artemieva, J. A., Rusakova, E. P., ve Abashidze, A. H. (2017). Modern trends and challenges of development of global aluminum industry, Metalurgija, 56(1-2), 255-258.
  • Durmaz, N. ve Lee, J. (2015). An empirical analysis of import demand function for Turkey: An ARDL bounds testing approach, The Journal of Developing Areas, 215-226.
  • Eşidir, K. A., Gür, Y. E., Yoğunlu, V. ve Çubuk, M. (2022). Yapay Sinir Ağları (YSA) ve ARIMA Modelleri ile Türkiye’de Aylık Sıfır km Otomobil Satış Adetlerinin Tahmin Edilmesi, Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 9(2), 260-277. doi: 10.47097/piar.1132101
  • Gujarati, D. N. (2003). Basic Econometrics, McGraw Hill, Newyork.
  • Jimenez-Martinez, M. ve Alfaro-Ponce, M. (2021). Fatigue Life Prediction of Aluminum Using Artificial Neural Network, Engineering Letters, 29(2), 1-6.
  • Khalil, D. M. ve Hamad, S. R. (2023). A Comparison of Artificial Neural Network Models and Time Series Models for Forecasting Turkey’s Monthly Aluminium Exports to Iraq, Journal of Survey in Fisheries Sciences, 10(1S), 4262-4279.
  • Li, X., Sengupta, T., Mohammed, K. S. ve Jamaani, F. (2023). Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods, Resources Policy, 82, 103580.
  • Liao, X. L., Xu, W. F. ve Gao, Z. Q. (2008). Application of artificial neural network to forecast the tensile fatigue life of carbon material, In Key Engineering Materials, Vol. 385, 533-536.
  • Luchko, M. R., Dziubanovska, N. ve Arzamasova, O. (2021). Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Vol. 2, 916-923.
  • Miikkulainen, R. (2010), Topology of a Neural Network, Boston, MA: Springer US, 988–989.
  • Özbay, İ. ve Kavaklı, M. (2008), Alüminyum Sektörü Endüstriyel Atıksu Arıtma Tesislerinin Kontrolü Ve İşletme Sorunlarının Çözümlerine Yönelik Uygulanabilir Öneriler, Karadeniz Uluslararası Çevre Sempozyumu, 25-29 Ağustos, Giresun, 171-184.
  • Quan, G. Z., Lv, W. Q., Mao, Y. P., Zhang, Y. W. ve Zhou, J. (2013). Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network, Materials & Design, 50, 51-61.
  • Sevigné-Itoiz, E., Gasol, C. M., Rieradevall, J. ve Gabarrell, X. (2014). Environmental consequences of recycling aluminum old scrap in a global market. Resources, conservation and recycling, 89, 94-103.
  • Sverdrup, H. U., Ragnarsdottir, K. V. ve Koca, D. (2015). Aluminium for the future: Modelling the global production, market supply, demand, price and long term development of the global reserves, Resources, Conservation and Recycling, 103, 139-154.
  • Syed, A. A. S., Kiran, H., ve Qureshi, S. (2022). Forecasting Group-Wise Imports And Exports Of Pakistan, Pakistan Journal Of Applied Economics, 32(2), 169-190.
  • Taud, H. ve Mas, J. F. (2018). Multilayer perceptron (MLP), Geomatic approaches for modeling land change scenarios, 451-455.
  • Tian, Z., Wong, L. ve Safaei, N. (2010), A neural network approach for remaining useful life prediction utilizing both failure and suspension histories, Mechanical Systems and Signal Processing, vol. 24, no. 5, 1542–1555.
  • Türkiye Cumhuriyet Merkez Bankası Elektronik Veri Dağıtım Sistemi (TCMB-EVDS), https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket, , Erişim Tarihi: 24.05.2023.
  • Türkiye İstatistik Kurumu (TÜİK), www.tuik.gov.tr, Erişim Tarihi: 25.05.2023.
  • Utonga ve Dimoso, (2019), The Nexus Between Export and Economic Growth in Tanzania, Journal of Business School, vol.2, issue.6, pp.49-59.
  • Wang, W., Chen, W. Q., Diao, Z. W., Ciacci, L., Pourzahedi, L., Eckelman, M. J., ... ve Shi, L. (2021). Multidimensional analyses reveal unequal resource, economic, and environmental gains and losses among the global aluminum trade leaders, Environmental Science & Technology, 55(10), 7102-7112.
  • Yağcı, T., Cöcen, Ü., Çulha, O. ve Korkmaz, A. (2021). Alüminyum Döküm Alaşımlarına Dair Son Yıllardaki Akademik Ve Endüstriyel Gelişmelere Genel Bakış Ve Değerlendirme, Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(3), 1191-1210.
  • Yang, H., Li, M., Bu, H., Lu, X., Yang, H., ve Qian, Z. (2022). Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application, Journal of Materials Engineering and Performance, 1-12.

MULTILAYER PERCEPTRON (MLP) İLE TÜRKİYE İŞLENMEMİŞ ALÜMİNYUM SEKTÖRÜ İTHALAT TAHMİNİ: 2023 YILI NİSAN-ARALIK AYLARI DÖNEMİ ÜZERİNE BİR İNCELEME

Year 2024, Issue: 68, 57 - 64, 31.08.2024

Kamil Abdullah Eşidir , Yunus Emre Gür

https://doi.org/10.18070/erciyesiibd.1310116

Abstract

Bu çalışma, Yapay Sinir Ağı modellerinden biri olan Multilayer Perceptron (MLP) kullanarak Türkiye’nin işlenmemiş alüminyum sektöründeki gelecekteki ithalat değerini tahmin etmeyi hedeflemektedir. Çalışma, Nisan-Aralık 2023 dönemi için ithalat tahminlerine odaklanarak, Türkiye'nin önemli bir endüstriyel sektörü olan işlenmemiş alüminyum sektörünün ekonomik büyümeye katkısını değerlendirmeyi amaçlamaktadır. Doğru bir şekilde ithalat miktarını tahmin etmek, sektörün gelecekteki stratejik planlamasını ve alınacak tedbirleri belirlemek açısından büyük önem arz etmektedir.
Çalışmada, “Türkiye İstatistik Kurumu (TÜİK)” ve “Türkiye Cumhuriyet Merkez Bankası (TCMB)” tarafından sağlanan veriler derlenmiştir. Sonuçlar, MLP modelinin işlenmemiş alüminyum sektörü ithalat tahminlerinde etkili bir araç olduğunu göstermiştir. Tahminler, 2023 Nisan-Aralık dönemi için Türkiye'nin aylık işlenmemiş alüminyum ithalatının 285 ile 322 milyon dolar arasında gerçekleşmesinin beklendiğini göstermiştir. Bu tahminler, sektörün gelecekteki stratejik kararlarını ve politikalarını belirlemek isteyen yöneticilere ve planlamacılara yol gösterebilir. Ayrıca, MLP modelinin endüstri ve ekonomik tahminleme alanında kullanım potansiyelinin başarısını da vurgulamaktadır.

Keywords

Yapay Sinir Ağları MultiLayer Perceptron (MLP) Arima Zaman Serileri İşlenmemiş Alüminyum

References

  • Adıgüzel, M. (2022). Dünya’da ve Türkiye’de Alüminyum Sektörü, Dış Ticareti ve Türkiye’nin Rekabet Gücü, Üçüncü Sektör Sosyal Ekonomi Dergisi, 57(4), 2782-2813. doi:10.15659/3.sektor-sosyal-ekonomi.22.11.1821
  • Alobaidi, D. (2022). Evaluation of Automobile Demand Forecast In Turkey Using Artifıcial Neural Networks (Doctoral dissertation). Karabuk University Institute of Graduate Programs Department of Industrial Engineering, Master Thesis, Karabuk.
  • Amiri, A., ve Gerdtham, U. (2011). Relationship between exports, imports, and economic growth in France: evidence from cointegration analysis and Granger causality with using geostatistical models. MPRA Paper No. 34190. Retrieved from http://mpra.ub.uni-muenchen.de/34190/. Erişim Tarihi:25.05.2023.
  • Bakari, S. ve Mabrouki, M. (2017).The nexus between exports, imports, Domestic investment and Economic growth in Japan, Economic Policy, (2116), 0–33. doi.org/10.1227/01.NEU.0000349921.14519.2A
  • Bishop, C. M. (1995), Neural networks for pattern recognition, Oxford University Press, Oxford 482.
  • Campa, J. ve Goldberg, L. S. (1995). Investment in manufacturing, exchange rates and external exposure, Journal of International Economics, 38(3-4), 297-320.
  • Çırakoğlu, Z. (2022), İstanbul Demir ve Demir Dışı Metaller İhracatçıları Birliği (İDDMİB) Alüminyum Sektörü Şubat 2022 Değerlendirmesi, https://turkishmetals.org/storage/files/ihracat_files/1646729168.pdf Erişim Tarihi: 10.05.2023.
  • Demirci, K. M. (2012). Dünya alüminyum ticaretinde Türkiye’nin yeri, Türk Mühendis ve Mimar Odaları Birliği Metalürji Mühendisleri Odası, 17-29.
  • Du, K-L ve Swamy, M. N. S. (2014), Neuronal networks and statistical learning, Springer, Berlin.
  • Dudin, M. N., Voykova, N. A., Frolova, E. E., Artemieva, J. A., Rusakova, E. P., ve Abashidze, A. H. (2017). Modern trends and challenges of development of global aluminum industry, Metalurgija, 56(1-2), 255-258.
  • Durmaz, N. ve Lee, J. (2015). An empirical analysis of import demand function for Turkey: An ARDL bounds testing approach, The Journal of Developing Areas, 215-226.
  • Eşidir, K. A., Gür, Y. E., Yoğunlu, V. ve Çubuk, M. (2022). Yapay Sinir Ağları (YSA) ve ARIMA Modelleri ile Türkiye’de Aylık Sıfır km Otomobil Satış Adetlerinin Tahmin Edilmesi, Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 9(2), 260-277. doi: 10.47097/piar.1132101
  • Gujarati, D. N. (2003). Basic Econometrics, McGraw Hill, Newyork.
  • Jimenez-Martinez, M. ve Alfaro-Ponce, M. (2021). Fatigue Life Prediction of Aluminum Using Artificial Neural Network, Engineering Letters, 29(2), 1-6.
  • Khalil, D. M. ve Hamad, S. R. (2023). A Comparison of Artificial Neural Network Models and Time Series Models for Forecasting Turkey’s Monthly Aluminium Exports to Iraq, Journal of Survey in Fisheries Sciences, 10(1S), 4262-4279.
  • Li, X., Sengupta, T., Mohammed, K. S. ve Jamaani, F. (2023). Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods, Resources Policy, 82, 103580.
  • Liao, X. L., Xu, W. F. ve Gao, Z. Q. (2008). Application of artificial neural network to forecast the tensile fatigue life of carbon material, In Key Engineering Materials, Vol. 385, 533-536.
  • Luchko, M. R., Dziubanovska, N. ve Arzamasova, O. (2021). Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Vol. 2, 916-923.
  • Miikkulainen, R. (2010), Topology of a Neural Network, Boston, MA: Springer US, 988–989.
  • Özbay, İ. ve Kavaklı, M. (2008), Alüminyum Sektörü Endüstriyel Atıksu Arıtma Tesislerinin Kontrolü Ve İşletme Sorunlarının Çözümlerine Yönelik Uygulanabilir Öneriler, Karadeniz Uluslararası Çevre Sempozyumu, 25-29 Ağustos, Giresun, 171-184.
  • Quan, G. Z., Lv, W. Q., Mao, Y. P., Zhang, Y. W. ve Zhou, J. (2013). Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1V alloy by artificial neural network, Materials & Design, 50, 51-61.
  • Sevigné-Itoiz, E., Gasol, C. M., Rieradevall, J. ve Gabarrell, X. (2014). Environmental consequences of recycling aluminum old scrap in a global market. Resources, conservation and recycling, 89, 94-103.
  • Sverdrup, H. U., Ragnarsdottir, K. V. ve Koca, D. (2015). Aluminium for the future: Modelling the global production, market supply, demand, price and long term development of the global reserves, Resources, Conservation and Recycling, 103, 139-154.
  • Syed, A. A. S., Kiran, H., ve Qureshi, S. (2022). Forecasting Group-Wise Imports And Exports Of Pakistan, Pakistan Journal Of Applied Economics, 32(2), 169-190.
  • Taud, H. ve Mas, J. F. (2018). Multilayer perceptron (MLP), Geomatic approaches for modeling land change scenarios, 451-455.
  • Tian, Z., Wong, L. ve Safaei, N. (2010), A neural network approach for remaining useful life prediction utilizing both failure and suspension histories, Mechanical Systems and Signal Processing, vol. 24, no. 5, 1542–1555.
  • Türkiye Cumhuriyet Merkez Bankası Elektronik Veri Dağıtım Sistemi (TCMB-EVDS), https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket, , Erişim Tarihi: 24.05.2023.
  • Türkiye İstatistik Kurumu (TÜİK), www.tuik.gov.tr, Erişim Tarihi: 25.05.2023.
  • Utonga ve Dimoso, (2019), The Nexus Between Export and Economic Growth in Tanzania, Journal of Business School, vol.2, issue.6, pp.49-59.
  • Wang, W., Chen, W. Q., Diao, Z. W., Ciacci, L., Pourzahedi, L., Eckelman, M. J., ... ve Shi, L. (2021). Multidimensional analyses reveal unequal resource, economic, and environmental gains and losses among the global aluminum trade leaders, Environmental Science & Technology, 55(10), 7102-7112.
  • Yağcı, T., Cöcen, Ü., Çulha, O. ve Korkmaz, A. (2021). Alüminyum Döküm Alaşımlarına Dair Son Yıllardaki Akademik Ve Endüstriyel Gelişmelere Genel Bakış Ve Değerlendirme, Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(3), 1191-1210.
  • Yang, H., Li, M., Bu, H., Lu, X., Yang, H., ve Qian, Z. (2022). Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application, Journal of Materials Engineering and Performance, 1-12.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Time-Series Analysis, Econometrics (Other)
Journal Section Makaleler
Authors

Kamil Abdullah Eşidir 0000-0002-8106-1758

Yunus Emre Gür 0000-0001-6530-0598

Early Pub Date August 22, 2024
Publication Date August 31, 2024
Acceptance Date April 29, 2024
Published in Issue Year 2024 Issue: 68

Cite

APA Eşidir, K. A., & Gür, Y. E. (2024). MULTILAYER PERCEPTRON (MLP) İLE TÜRKİYE İŞLENMEMİŞ ALÜMİNYUM SEKTÖRÜ İTHALAT TAHMİNİ: 2023 YILI NİSAN-ARALIK AYLARI DÖNEMİ ÜZERİNE BİR İNCELEME. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(68), 57-64. https://doi.org/10.18070/erciyesiibd.1310116
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