| Summary: | Recent maritime legislation demands the transformation of the transportation sector to greener and more energy efficient. Liquified natural gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have focused on the prediction of the LNG price; however, no previous work has been carried out on the forecast of the spot charter rate of LNG carrier ships, an important factor for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the general regression neural network presented a stable overall performance for forecasting periods of 2, 4 and 6 months ahead.
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