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Related Experiment Videos

Forecasting China's shipping indices based on modal decomposition and optimized deep learning integrated model.

Yuye Zou1, Yingyu Liu1, Guangnian Xiao1

  • 1College of Economics and Management, Shanghai Maritime University, Shanghai, China.

Plos One
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces the VMD-CPSO-BiLSTM model for accurate shipping index forecasting in China. This hybrid approach enhances predictions for maritime sector trends.

Area of Science:

  • Maritime Economics
  • Financial Time Series Analysis
  • Deep Learning

Background:

  • Accurate forecasting of shipping indices is crucial for China's maritime sector.
  • Traditional models struggle with the nonlinearity and non-stationarity of shipping data.

Purpose of the Study:

  • To develop an innovative hybrid forecasting model, VMD-CPSO-BiLSTM, to enhance prediction accuracy for Chinese shipping indices.
  • To address the challenges of nonlinearity, non-stationarity, and multi-scale characteristics in time series forecasting.

Main Methods:

  • Variational Mode Decomposition (VMD) to decompose time series into intrinsic mode functions (IMFs).
  • Chaotic Particle Swarm Optimization (CPSO) to optimize Bi-directional Long Short-Term Memory (BiLSTM) network parameters.
  • Integration of predictions from high-frequency and low-frequency components for comprehensive forecasts.

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Main Results:

  • The VMD-CPSO-BiLSTM model demonstrated superior performance compared to conventional single deep learning models and other hybrid approaches.
  • The model effectively captured nonlinear and complex patterns in shipping index data.
  • Empirical validation using key Chinese shipping indices confirmed the model's enhanced predictive accuracy and stability.

Conclusions:

  • The VMD-CPSO-BiLSTM model offers a reliable tool for forecasting shipping market trends.
  • The model provides enhanced decision-making support for strategic planning and operational management in the maritime industry.
  • This methodological innovation contributes significantly to maritime economics and financial time series analysis.