Shipping market forecasting by forecast combination mechanism

The volatile characteristics of the tanker market pose challenges to forecasting. In addition, the volatile characteristics of newbuilding and secondhand ship prices, time charter rates, and scrap values make developing a unified framework of forecasting difficult. Most researchers have developed forecasting models and evaluated their performance based on a specific market. Such narrow development imposes difficulty for practitioners to choose a suitable model. Due to the boom of machine learning, many researchers are trying to boost the forecasting accuracy of shipping markets using machine learning. However, there are many hyper-parameters of the complex machine learning models and a slight variation of the model may cause significant performance degradation. This paper utilizes a forecast combination mechanism to forecast many time series collected from the shipping market, including newbuilding and secondhand ship prices, scrap values, and time charter rates. The models inside the combination pool are just linear functions. Finally, we compare their performance with conventional machine learning models and naive forecasts using three error metrics and statistical tests. The statistical tests show that the combination of linear models is superior. The findings of this study also indicate that complex models do not boost forecasting accuracy necessarily.