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Forecasting tanker freight rate using neural networks

Author

Listed:
  • Jun Li
  • Michael G. Parsons

Abstract

Improvement in forecasting accuracy is a difficult task but critical for business success. This paper investigates the potential of neural networks for short- to long-term prediction of monthly tanker freight rates. Procedures are outlined for the development of the neural networks. The problem of under-training and over-training is addressed by controlling the number of iterations during the training process of neural networks. A comparative study of predictive performance between neural networks and ARMA time series models is conducted. Our evience shows that neural networks can significantly outperform time series models, especially for longer-term forecasting.

Suggested Citation

  • Jun Li & Michael G. Parsons, 1997. "Forecasting tanker freight rate using neural networks," Maritime Policy & Management, Taylor & Francis Journals, vol. 24(1), pages 9-30, January.
    • Handle: RePEc:taf:marpmg:v:24:y:1997:i:1:p:9-30
    DOI: 10.1080/03088839700000053
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    Cited by:

    1. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    2. Dimitrios Lyridis & Nikolaos Manos & Panayotis Zacharioudakis & Athanassios Pappas & Aristidis Mavris, 2017. "Measuring Tanker Market Future Risk with the use of FORESIM," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 38-53, January-M.
    3. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
    4. Zaili Yang & Esin Erol Mehmed, 2019. "Artificial neural networks in freight rate forecasting," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(3), pages 390-414, September.
    5. Payman Eslami & Kihyo Jung & Daewon Lee & Amir Tjolleng, 2017. "Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 538-550, August.
    6. Mo, Jixian & Gao, Ruobin & Fai Yuen, Kum & Bai, Xiwen, 2024. "Predictive analysis of sell-and-purchase shipping market: A PIMSE approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    7. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.

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