Title: Time series forecasting of domestic shipping market: comparison of SARIMAX, ANN-based models and SARIMAX-ANN hybrid model
Authors: Cemile Solak Fiskin; Ozgu Turgut; Sjur Westgaard; A. Güldem Cerit
Addresses: Department of Maritime Business Administration, Ordu University, Ordu, Turkey ' Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway ' Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway ' Maritime Faculty, Dokuz Eylul University, İzmir, Turkey
Abstract: Seaborne transport forecasting has attracted substantial interest over the years because of providing a useful policy tool for decision-makers. Although various forecasting methods have been widely studied, there is still broad debate on accurate forecasting models and preprocessing. The current paper aims to point out these issues, as well as to establish the forecasting model of the domestic cargo volumes using SARIMAX, MLP, LSTM and NARX and SARIMAX-ANN hybrid models. Based on the domestic cargo volumes of Turkey, findings suggest that SARIMA-MLP models can be considered as an appropriate alternative, at least for time series forecasting of shipping. Pre-processed data provides a significant improvement over those obtained with unpreprocessed data, with the accuracy of the models found to be significantly boosted with the Fourier term of decomposition. The results indicate that SARIMAX-MLP, with a mean absolute percentage error (MAPE) of 4.81, outperforms the closest models of SARIMAX, with a MAPE of 6.14 and LSTM with Fourier decomposition with a MAPE of 6.52. Findings have implications for shipping policymakers to plan infrastructure development, and useful for shipowners in accurately formulating shipping demand.
Keywords: time series forecasting; shipping; artificial neural network; ARIMA; machine learning; hybrid model.
International Journal of Shipping and Transport Logistics, 2022 Vol.14 No.3, pp.193 - 221
Received: 12 Sep 2019
Accepted: 01 Mar 2020
Published online: 25 Apr 2022 *