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A New Container Throughput Forecasting Paradigm under COVID-19

Author

Listed:
  • Anqiang Huang

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
    Institute of Regulatory Science, Beijing Technology and Business University, Beijing 100048, China)

  • Xinjun Liu

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Changrui Rao

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Yi Zhang

    (School of Logistics, Beijing Wuzi University, Beijing 101149, China)

  • Yifan He

    (Institute of Regulatory Science, Beijing Technology and Business University, Beijing 100048, China)

Abstract

COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition–ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.

Suggested Citation

  • Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2990-:d:763788
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