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Forecasting shipping index using CEEMD-PSO-BiLSTM model

Author

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  • Chengang Li
  • Xuan Wang
  • Yongxiang Hu
  • Ying Yan
  • Han Jin
  • Guofei Shang

Abstract

Shipping indices are extremely volatile, non-stationary, unstructured and non-linear, and more difficult to forecast than other common financial time series. Based on the idea of "decomposition-reconstruction-integration", this article puts forward a combined forecasting model CEEMD-PSO-BiLSTM for shipping index, which overcomes the linearity limitation of traditional models. CEEMD is used to decompose the original sequence into several IMF components and RES sequences, and the IMF components are recombined by reconstruction. Each sub-sequence is predicted and analyzed by PSO-BiLSTM neural network, and finally the predicted value of the original sequence is obtained by summing up the predicted values of each sub-sequence. Using six major shipping indices in China’s shipping market such as FDI and BDI as test data, a systematic comparison test is conducted between the CEEMD-PSO-BiLSTM model and other mainstream time-series models in terms of forecasting effects. The results show that the model outperforms other models in all indicators, indicating its universality in different shipping markets. The research results of this article can deepen and improve the understanding of shipping indices, and also have important implications for risk management and decision management in the shipping market.

Suggested Citation

  • Chengang Li & Xuan Wang & Yongxiang Hu & Ying Yan & Han Jin & Guofei Shang, 2023. "Forecasting shipping index using CEEMD-PSO-BiLSTM model," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-22, February.
    • Handle: RePEc:plo:pone00:0280504
    DOI: 10.1371/journal.pone.0280504
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    1. Fernando Perez-cruz & Julio Afonso-rodriguez & Javier Giner, 2003. "Estimating GARCH models using support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 163-172.
    2. K P B Cullinane & K J Mason & M Cape, 1999. "A Comparison of Models for Forecasting the Baltic Freight Index: Box-Jenkins Revisited," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 1(2), pages 15-39, December.
    3. Shun Chen & Hilde Meersman & Eddy van de Voorde, 2012. "Forecasting spot rates at main routes in the dry bulk market," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 14(4), pages 498-537, December.
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