Authors: Aylin Caliskan; Burcu Karaöz
Addresses: Faculty of Business, Department of International Logistics Management, Yaşar University, 35100, İzmir, Turkey ' Faculty of Business, Department of International Logistics Management, Yaşar University, 35100, İzmir, Turkey
Abstract: The main aim of this study is to forecast the likelihood of increasing or decreasing port throughput from month to month with determined market indicators as input variables. Additionally, the other aim is to determine whether artificial neural network (ANN) and support vector machines (SVM) algorithms are capable of accurately predicting the movement of port throughput. To this aim, Turkish ports were chosen as research environment. The monthly average exchange rates of US dollar, euro, and gold (compared to Turkish lira), and crude oil prices were used as market indicators in the prediction models. The experimental results reveal that, the model with specific market indicators, successfully forecasts the direction of movement on port throughput with accuracy rate of 90.9% in ANN and accuracy rate of 84.6% in SVM. The model developed in the research may help managers to develop short-term logistics plans in operational processes and may help researchers in terms of adapting the model to other research areas.
Keywords: port throughput; predicting; forecasting in shipping; artificial neural network; ANN; support vector machine; SVM.
International Journal of Data Mining, Modelling and Management, 2019 Vol.11 No.1, pp.45 - 63
Received: 01 May 2017
Accepted: 21 Mar 2018
Published online: 11 Oct 2018 *