Araştırma Makalesi
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Ensemble Modelleriyle Çin Konteyner Navlun Endeksi'nin Tahmin Edilmesi

Yıl 2023, Cilt: 8 Sayı: 2, 416 - 423, 30.12.2023

Öz

Konteyner taşımacılığında navlun oranlarının nasıl değişeceğini sektördeki paydaşların öngörebilmesi oldukça önemlidir. Bu çalışma, literatürde ilk defa olmak üzere, konteyner taşımacılığında navlun oranlarının değişimini gösteren en önemli göstergelerden biri olan CCFI (Çin Konteyner Navlun Endeksi) 'nin tahmini için toplu zaman serisi modelleri sunmaktadır. Çalışmanın sonuçları, modellerin CCFI’nin tahmininde oldukça iyi sonuçlar verdiğini ve önemli bir karar destek sistemi olarak kullanılabileceğini göstermektedir.

Kaynakça

  • Chen, Y., Liu, B., Wang, T. (2021). Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA, Grey Systems: Theory and Application, 11(3), 358-371.
  • Clarksons Research (2023). Shipping Intelligence Network, (14.07.2023), Retrieved from https://sin.clarksons.net/
  • Deng, Y., and Yang, J. (2021). Research on the Co-integration Relationship between China Coastal Bulk Freight Index and the Freight Rate of Sample Route, 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), Guangzhou, China, pp. 1373-1377.
  • Fan, L. and Yin, J. (2015). Analysis of structural changes in container shipping, Maritime Economics & Logistics, 18(2), 174–191.
  • Fei, Y., and Zhou Y. (2023). Intelligent Prediction Model of Shanghai Composite Index Based on Technical Indicators and Big Data Analysis, Highlights in Business, Economics and Management, 17.,
  • Ghareeb, A. (2023). Time Series Forecasting of Stock Price for Maritime Shipping Company in COVID-19 Period Using Multi-Step Long Short-Term Memory (LSTM) Networks, Scieondo, 17, 1728 – 1747.
  • Hirata, E., Matsuda, T. (2022). Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment", Journal of Marine Science and Engineering, 10 (593).
  • ICS, Institute of Chartered Shipbrokers (2015). Liner Trades, London: Institute of Chartered Shipbrokers.
  • Jeon, J., Duru, O., Yeo, G. (2019). Modeling cyclic container freight index using system dynamics, Maritime Policy & Management, 47(3), 287–303.
  • Koyuncu, K., Tavacıoğlu, L., Gökmen, N., Arıcan, U.Ç. (2021). Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models, Maritime Policy and Management, 48(8), 1–13.
  • Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Manning Publications.
  • Luo, M., Fan, L., Liu, L. (2009). An econometric analysis for container shipping market, Maritime Policy and Management, 36(6), 507–523.
  • Munim, Z.H., and H.J. Schramm. (2017). Forecasting container shipping freight rates for the Far East Northern Europe trade lane. Maritime Economics & Logistics 19 (1), 106–125.
  • Munim, Z.H. and Schram, H.J. (2021). Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models, Maritime Economics & Logistics, 23, 310–327.
  • Nielsen, P., L. Jiang, N.G.M. Rytter, and G. Chen. (2014). An investigation of forecast horizon and observation ft's influence on an econometric rate forecast model in the liner shipping industry, Maritime Policy & Management 41 (7), 667–682.
  • Notteboom, T. (2012). Container Shipping. in The Blackwell Companion to Maritime Economics, Wiley-Blackwell, Oxford, UK, 230-262.
  • Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method, Transport Policy, 133, 86-107.
  • Schramm, H. J. and Munim, Z.H. (2021). Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners, Research in Transportation Business & Management, 41.
  • SSE, Shanghai Shipping Exchange. (2023). (15.08.2023), Retrieved from https://en.sse.net.cn/indices/introduction_ccfi_new.jsp
  • Stopford, M. 2008. Maritime Economics, 3rd ed. London: Routledge.
  • Tu, X., Yang, Y., Lin, Y., Ma, S. (2023). Analysis of influencing factors and prediction of China's Containerized Freight Index. Front. Mar. Sci. 10:1245542.
  • UNCTAD. (2021). Review of Maritime Transport 2021. Geneva.
  • Yifei, Z., Z. Dali, and T. Yanagita (2018). Container liner freight index based on data from e-booking platforms. Maritime Policy & Management 45 (6): 739–755.

Forecasting the China Container Freight Index with Ensemble Models

Yıl 2023, Cilt: 8 Sayı: 2, 416 - 423, 30.12.2023

Öz

Stakeholders in the sector need to be able to predict how freight rates will change in container transportation. For the first time in the literature, this study presents aggregate time series models for the prediction of CCFI (China Container Freight Index), one of the most critical indicators showing the change of freight rates in container shipping. The study results show that the models provide promising results in forecasting CCFI and can be used as an essential decision support system.

Kaynakça

  • Chen, Y., Liu, B., Wang, T. (2021). Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA, Grey Systems: Theory and Application, 11(3), 358-371.
  • Clarksons Research (2023). Shipping Intelligence Network, (14.07.2023), Retrieved from https://sin.clarksons.net/
  • Deng, Y., and Yang, J. (2021). Research on the Co-integration Relationship between China Coastal Bulk Freight Index and the Freight Rate of Sample Route, 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), Guangzhou, China, pp. 1373-1377.
  • Fan, L. and Yin, J. (2015). Analysis of structural changes in container shipping, Maritime Economics & Logistics, 18(2), 174–191.
  • Fei, Y., and Zhou Y. (2023). Intelligent Prediction Model of Shanghai Composite Index Based on Technical Indicators and Big Data Analysis, Highlights in Business, Economics and Management, 17.,
  • Ghareeb, A. (2023). Time Series Forecasting of Stock Price for Maritime Shipping Company in COVID-19 Period Using Multi-Step Long Short-Term Memory (LSTM) Networks, Scieondo, 17, 1728 – 1747.
  • Hirata, E., Matsuda, T. (2022). Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment", Journal of Marine Science and Engineering, 10 (593).
  • ICS, Institute of Chartered Shipbrokers (2015). Liner Trades, London: Institute of Chartered Shipbrokers.
  • Jeon, J., Duru, O., Yeo, G. (2019). Modeling cyclic container freight index using system dynamics, Maritime Policy & Management, 47(3), 287–303.
  • Koyuncu, K., Tavacıoğlu, L., Gökmen, N., Arıcan, U.Ç. (2021). Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models, Maritime Policy and Management, 48(8), 1–13.
  • Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Manning Publications.
  • Luo, M., Fan, L., Liu, L. (2009). An econometric analysis for container shipping market, Maritime Policy and Management, 36(6), 507–523.
  • Munim, Z.H., and H.J. Schramm. (2017). Forecasting container shipping freight rates for the Far East Northern Europe trade lane. Maritime Economics & Logistics 19 (1), 106–125.
  • Munim, Z.H. and Schram, H.J. (2021). Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models, Maritime Economics & Logistics, 23, 310–327.
  • Nielsen, P., L. Jiang, N.G.M. Rytter, and G. Chen. (2014). An investigation of forecast horizon and observation ft's influence on an econometric rate forecast model in the liner shipping industry, Maritime Policy & Management 41 (7), 667–682.
  • Notteboom, T. (2012). Container Shipping. in The Blackwell Companion to Maritime Economics, Wiley-Blackwell, Oxford, UK, 230-262.
  • Saeed, N., Nguyen, S., Cullinane, K., Gekara, V., Chhetri, P. (2023). Forecasting container freight rates using the Prophet forecasting method, Transport Policy, 133, 86-107.
  • Schramm, H. J. and Munim, Z.H. (2021). Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners, Research in Transportation Business & Management, 41.
  • SSE, Shanghai Shipping Exchange. (2023). (15.08.2023), Retrieved from https://en.sse.net.cn/indices/introduction_ccfi_new.jsp
  • Stopford, M. 2008. Maritime Economics, 3rd ed. London: Routledge.
  • Tu, X., Yang, Y., Lin, Y., Ma, S. (2023). Analysis of influencing factors and prediction of China's Containerized Freight Index. Front. Mar. Sci. 10:1245542.
  • UNCTAD. (2021). Review of Maritime Transport 2021. Geneva.
  • Yifei, Z., Z. Dali, and T. Yanagita (2018). Container liner freight index based on data from e-booking platforms. Maritime Policy & Management 45 (6): 739–755.

Ayrıntılar

Birincil Dil İngilizce
Konular Deniz Taşımacılığı ve Nakliye Hizmetleri
Bölüm Araştırma Makalesi
Yazarlar

Tolga TUZCUOĞLU 0000-0002-5269-9701

Hüseyin GENCER 0000-0002-4945-4420

Erken Görünüm Tarihi 27 Ekim 2023
Yayımlanma Tarihi 30 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 2

Kaynak Göster

APA TUZCUOĞLU, T., & GENCER, H. (2023). Forecasting the China Container Freight Index with Ensemble Models. JOEEP: Journal of Emerging Economies and Policy, 8(2), 416-423.

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