This paper compares different univariate forecasting methods and provides a more accurate short-term forecasting model for container throughput to create a reference for relevant authorities. Six different univariate methods, including the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecasting model, the hybrid grey forecasting model, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the SARIMA model is a reliable forecasting method for forecasting container throughput with seasonal variations. This study’s findings can help to predict the near-future demand for container throughput at international ports.
Huang, Juan; Chu, Ching-Wu; and Tsai, Yi-Chen
"CONTAINER THROUGHPUT FORECASTING FOR INTERNATIONAL PORTS IN TAIWAN,"
Journal of Marine Science and Technology: Vol. 28:
5, Article 15.
Available at: https://jmstt.ntou.edu.tw/journal/vol28/iss5/15