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


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.

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  1. Batchelor, R., Alizadeh, A.H. and Visvikis, I.D. (2007) Forecasting spot and forward prices in the international freight market. International Journal of Forecasting 23 (1): 101–114.

    Article  Google Scholar 

  2. Bulut, E., Duru, O. and Yoshid, S. (2012) A fuzzy integrated logical forecasting (FILF) model of time charter rates in dry bulk shipping: A vector autoregressive design of fuzzy time series with fuzzy c-means clustering. Maritime Economics & Logistics 14 (3): 300–318.

    Article  Google Scholar 

  3. Chen, S., Meersman, H. and van de Voorde, E. (2012) Forecasting spot rates at main routes in the dry bulk market. Maritime Economics & Logistics 14 (4): 498–537.

    Article  Google Scholar 

  4. Duru, O., Bulut, E. and Yoshida, S. (2012) A fuzzy extended Delphi method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case. Expert Systems with Applications 39 (1): 840–848.

    Article  Google Scholar 

  5. Goulielmos, A.M. and Psifia, M.E. (2009) Forecasting weekly freight rates for one-year time charter 65 000 dwt bulk carrier, 1989–2008, using nonlinear methods. Maritime Policy & Management 36 (5): 411–436.

    Article  Google Scholar 

  6. Goulielmos, A.M. and Psifia, M.E. (2013) Forecasting short-term freight rate cycles: Do we have a more appropriate method than a normal distribution? Maritime Policy & Management 38 (6): 645–672.

    Article  Google Scholar 

  7. Guhathakurta, K., Mukherjee, I. and Chowdhury, A.R. (2008) Empirical mode decomposition analysis of two different financial time series and their comparison. Chaos, Solitons & Fractals 37 (4): 1214–1227.

    Article  Google Scholar 

  8. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H. and Zheng, Q. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. In: Proceedings of the Royal Society of London Series A – Mathematical Physical And Engineering Sciences, Series A. London: The Royal Society Publishing, 454: 903–995.

  9. Huang, N.E., Shen, Z. and Long, S.R. (1999) A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics 31: 417–457.

    Article  Google Scholar 

  10. Jing, L., Marlow, P.B. and Hui, W. (2008) An analysis of freight rate volatility in dry bulk shipping markets. Maritime Policy and Management 35 (3): 237–251.

    Article  Google Scholar 

  11. Kavussanos, M.G. and Nomikos, N.K. (2003) Price discovery, causality and forecasting in the freight futures market. Review of Derivatives Research 6 (3): 203–230.

    Article  Google Scholar 

  12. Leonova, Y. and Nikolov, V. (2012) A wavelet and neural network model for the prediction of dry bulk shipping indices. Maritime Economics & Logistics 14 (3): 319–333.

    Article  Google Scholar 

  13. Premanode, B. and Toumazou, C. (2013) Improving prediction of exchange rates using differential EMD. Expert Systems with Applications 40 (1): 377–384.

    Article  Google Scholar 

  14. Qi, K.Y., He, Z.J. and Zi, Y.Y. (2007) Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis. Mechanical Systems and Signal Processing 21 (7): 2750–2760.

    Article  Google Scholar 

  15. Rojas, A. et al (2012) Application of empirical mode decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease. Expert Systems with Applications 40: 2756–2766.

    Article  Google Scholar 

  16. Stopford, M. (2009) Maritime Economics, 3rd edn. Oxon, UK: Routledge Press.

    Book  Google Scholar 

  17. Wei, Y. and Chen, M.C. (2012) Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C 21 (1): 148–162.

    Article  Google Scholar 

  18. Yu, L., Wang, S.Y and Lai, K.K. (2008) Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics 30 (5): 2623–2635.

    Article  Google Scholar 

  19. Zeng, Q. and Qu, C. (2014) An approach for Baltic Dry Index analysis based on empirical mode decomposition. Maritime Policy and Management 41 (3): 224–240.

    Article  Google Scholar 

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The authors would like to thank the anonymous referees for their valuable suggestions. This work is supported by the National Natural Science Foundation of China [Grant No: 71431001,71371037] and Talents Project of Liaoning [grant no 2013921075].

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Zeng, Q., Qu, C., Ng, A. et al. A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks. Marit Econ Logist 18, 192–210 (2016).

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  • dry bulk shipping market
  • empirical mode decomposition
  • artificial neural networks
  • forecasting
  • Baltic Dry Index (BDI)