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A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model

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Maritime Economics & Logistics Aims and scope

Abstract

World trade is growing constantly, facilitated by the fast expansion of logistics. However, risks and uncertainty in shipping have also increased, in dire need to be addressed by the research community, through more accurate and efficient methods of forecasting. In recent years, combining attention models and deep learning has produced remarkable results in various domains. With daily data spanning the period from January 6, 1995, to September 16, 2022 (totaling 6896 observations), we predict the Baltic Dry Index (BDI) using a deep integrated model (CNN-BiLSTM-AM) comprising a convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM), and the attention mechanism (AM). Our findings indicate that the integrated model CNN-BiLSTM-AM encompasses the nonlinear and nonstationary characteristics of the shipping industry, and it has a greater prediction accuracy than any single model, with an R2 value of 96.9%. This research shows that focusing on the data’s value has a particular appeal in the intelligence era. The study enhances the integrated research of machine learning in the shipping business and offers a foundation for economic decisions.

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Acknowledgements

We appreciate the comments and opinions of MEL editors and reviewers; they were highly insightful and beneficial in updating and refining our work, as well as providing essential guidance for our research.

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Authors and Affiliations

  1. Graduate School of Technology Management, Kyunghee University, Seoul, Korea

    Miao Su

  2. Department of Trade and Logistics, Chung Ang University, 84 Heukseok-ro, Dongjak gu, Seoul, Korea

    Keun Sik Park & Sung Hoon Bae

  3. Samsung SDS, Seoul, Korea

    Sung Hoon Bae

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  1. Miao Su
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Correspondence to Sung Hoon Bae.

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Appendix

Appendix

Table 3 illustrates the specific information of the final model parameters.

Table 3 Final model parameters
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Su, M., Park, K.S. & Bae, S.H. A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model. Marit Econ Logist 26, 21–43 (2024). https://doi.org/10.1057/s41278-023-00278-6

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