Forecasting seasonal container throughput at international ports using SARIMA models

Abstract

Seasonal container throughput forecasts at ports are immensely important to logistics companies, shipping lines, port authorities and shipyards. Such forecasts allow shipping lines and port operators to formulate appropriate short-to-medium strategies in order to maintain competitiveness. Seasonal autoregressive integrated moving average (in short, SARIMA) models can be employed for this purpose to provide reliable seasonal forecasts of container throughput at a given container port. This article explores the use of SARIMA models in forecasting container throughput at several major international container ports, while taking into consideration seasonal variations. First, the SARIMA model development methodology is described. Second, a database consisting of monthly container port traffic data between 1999 and 2007 for international container ports is developed. Short-term container demand forecasting models are then developed for each of the top 20 international container ports for the purpose of monthly container throughput prediction. Through the use of various performance metrics, the effectiveness of the developed SARIMA models for these ports is evaluated. It is found that SARIMA models can produce reliable throughput forecasts at major international ports. Qualitative insights are then drawn, thereby allowing shipping and port operators to make better tactical and operational decisions.