A Comparison of SVM and Traditional Methods for Demand Forecasting in a Seaport: A case study

Abstract

Demand forecasting is important for planning future of a seaport facility. In this paper, different methods are compared for demand forecasting problem of a seaport in Turkey. Three types of data (general cargo, container, vehicle) were collected from the period of 2012-2017. Using machine learning for demand forecasting was found to be an important missing link in earlier studies and it was observed that the studies about demand forecasting on container terminals is a lot more than the studies on maritime terminals. Statistical forecasting methods and machine learning methods are applied for all types of data to determine the best estimation method and forecast the handling volumes for the next two years. The comparison of the forecasting performances of statistical forecasting methods and machine learning methods have been comparatively analysed. According to chosen accuracy measures, Multiplicative Holt Winter’s was recognized as the best forecasting method for container and vehicle handling volumes, whereas machine learning method ensured the best forecasting values for the general cargo.

Keywords: Demand forecasting, seaport, machine learning, statistical modeling

DOI: 10.7176/JSTR/5-3-19