This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

本研究旨在对不同的单变量预测方法进行比较，为集装箱吞吐量提供更准确的短期预测模型，以供相关部门参考。我们收集了亚洲三大港口上海、新加坡和釜山港的集装箱吞吐量月度数据。使用了六种不同的单变量方法，包括灰色预测模型、混合灰色预测模型、乘法分解模型、三角回归模型、季节性虚拟变量回归模型和季节性自回归综合移动平均（SARIMA）模型。我们发现混合灰色预测模型优于其他单变量模型。