Forecasting Models for the Cargo Throughput at Hong Kong Port and Kaohsiung Port

The import and export of goods normally require the involvement of many different sectors from purchasing, manufacturing, transporting, inventory, distribution, etc. In order to have proper plans, accurately forecast the volume of imported-exported goods is the core issue. By comparing the performance of autoregressive integrated moving average (ARIMA) model, Grey model, and their joint Fourier modified models, this paper aims at obtaining an efficient model to forecast the cargo throughput at a sea port. The accuracy of the conventional models is found to be significantly boosted with the Fourier modification. In the empirical case studies of cargo throughput at Hong Kong port and container throughput at Kaohsiung port, modified seasonal ARIMA models, respectively FSARIMA(4,1,4)(1,1,0)4 and FSARIMA(4,1,4)(0,1,0)12, are strongly suggested due to their satisfactorily high forecasting power. We further employ these models to provide the forecast in 2013 at the two ports in order to assist the control and scheduling of the two port systems and for the terminal operators in decision making and planning. Key-Words: ARIMA model, Grey Model, Cargo throughput, Container throughput, Fourier modification, Hong Kong port, Kaohsiung port