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.
Highlights
The results show that the proposed model can effectively capture the degree of impact of the new crown epidemic on container throughput, and the forecasting accuracy is higher than that of empirical mode decomposition (EMD)-support vector regression (SVR), SVR, and autoregressive integrated moving average (ARIMA)
This paper proposes a hybrid decomposition–ensemble forecasting model applying the event analysis to the container throughput forecasting tasks, which provides a powerful tool for forecasting tasks considering the impact of major events, e.g., COVID-19
IMF1 is composed of non-stationary data, theforecasting selection of lag period d willproposes effectively task to develop an effective container throughput model
Summary
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. In order to control the spread of the epidemic, governments around the world have taken different levels of preventive and control measures, which, while interrupting the spread of the virus, have negatively impacted maritime trade. Measures such as restrictions on ship activity and work stoppages can lead to a decline in transportation and hinder the development of the world economy [4,5].
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