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Forecasting shipping index using CEEMD-PSO-BiLSTM model.

Chengang Li1,2, Xuan Wang1,3, Yongxiang Hu4

  • 1School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.

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Summary
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Forecasting volatile shipping indices is challenging. A novel CEEMD-PSO-BiLSTM model decomposes, reconstructs, and integrates data, significantly improving prediction accuracy over traditional methods.

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Area of Science:

  • Maritime Economics
  • Financial Forecasting
  • Time Series Analysis

Background:

  • Shipping indices exhibit high volatility, non-stationarity, and non-linearity, posing forecasting challenges.
  • Traditional linear models are inadequate for capturing the complex dynamics of shipping market data.

Purpose of the Study:

  • To develop an advanced forecasting model for shipping indices.
  • To overcome the limitations of linear models in predicting volatile financial time series.

Main Methods:

  • A combined forecasting model, CEEMD-PSO-BiLSTM, is proposed, utilizing decomposition-reconstruction-integration.
  • Empirical Mode Decomposition (EMD) variants like CEEMD decompose the series, Particle Swarm Optimization (PSO) optimizes BiLSTM parameters, and BiLSTM networks forecast sub-sequences.

Main Results:

  • The CEEMD-PSO-BiLSTM model demonstrated superior performance across all tested indicators compared to mainstream time-series models.
  • Validation using six major Chinese shipping indices (e.g., FDI, BDI) confirmed the model's effectiveness and universality.

Conclusions:

  • The CEEMD-PSO-BiLSTM model offers a robust solution for forecasting complex shipping indices.
  • Findings provide valuable insights for risk and decision management in the global shipping market.