Shipping economic forecasting: recent developments, applications, and future directions

Abstract

Forecasting is vital in shipping economics and directly affects the business decisions of shipping companies and the quality development of the shipping markets. This study critically reviews variables, methods, and results used for shipping economic forecasting. This study provides an extensive review of the development of the shipping market forecasting models, which can be broadly categorised into artificial intelligence and classical economic models. Our review identifies forecasting applications in the following areas: freight markets, newbuilding and second-hand ship markets, and ship-demolition markets. We review the evolution of the forecasting methods over time and distinguish six types of feature engineering (i.e. the process of preparing and transforming input data) that improve model generalisation performance (i.e. ability for the model to work outside training data) in the existing literature. We further discuss the improvement, input determination, evaluation metrics, and hyper-parameter optimisation of models. Our analysis shows that support vector regression and artificial neural networks are the commonly used techniques; Grid search and evolutionary optimisation are popular for hyperparameter optimisation in current research. Finally, we discuss the achievements and limitations of the existing literature. The survey concludes with the identification of existing gaps and recommendations for future research.