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

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.

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Acknowledgements

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). https://doi.org/10.1057/mel.2015.2

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Keywords

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