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A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

Author

Listed:
  • Qingcheng Zeng

    (School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.)

  • Chenrui Qu

    (School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.)

  • Adolf K.Y. Ng

    (Department of Supply Chain Management, I.H. Asper School of Business, University of Manitoba, Winnipeg, Canada MB R3T 5V4.)

  • Xiaofeng Zhao

    (School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China.)

Abstract

In this article, a method based on empirical mode decomposition (EMD) and artificial neural networks (ANN) is developed for Baltic Dry Index (BDI) forecasting. The original BDI series is decomposed into several independent intrinsic mode functions (IMFs) using EMD first. Then the IMFs are composed into three components: short-term fluctuations, effect of extreme events and long-term trend. On the basis of results of decomposition and composition, ANN is used to model each IMF and composed component. Results show that the proposed EMD-ANN method outperforms ANN and VAR. The EMD-based method thus provides a useful technique for dry bulk market analysis and forecasting.

Suggested Citation

  • Qingcheng Zeng & Chenrui Qu & Adolf K.Y. Ng & Xiaofeng Zhao, 2016. "A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(2), pages 192-210, June.
    • Handle: RePEc:pal:marecl:v:18:y:2016:i:2:p:192-210
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    Citations

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    Cited by:

    1. 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.
    2. Lucía Inglada-Pérez & Pablo Coto-Millán, 2021. "A Chaos Analysis of the Dry Bulk Shipping Market," Mathematics, MDPI, vol. 9(17), pages 1-35, August.
    3. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    4. Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
    5. Joan Mileski & Christopher Clott & Cassia Bomer Galvao & Taliese Laverne, 2020. "Technical analysis: the psychology of the market of dry bulk freight rates," Journal of Shipping and Trade, Springer, vol. 5(1), pages 1-15, December.
    6. Christos Katris & Manolis G. Kavussanos, 2021. "Time series forecasting methods for the Baltic dry index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1540-1565, December.
    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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