Long-term prediction of liquefied natural gas spot freight rates

Spot freight rates of liquefied natural gas (LNG) carriers have become increasingly volatile due to rising demand for cargo transportation and dynamic market conditions. This study aims to enhance the accuracy of freight rate volatility forecasting using artificial intelligence, evaluating the performance of the gated recurrent unit (GRU) model for long-term monthly predictions. Key predictive variables, including LNG prices, LNG inventory levels, sailing speeds of LNG carriers, port call indices, and charter rates, were selected for analysis. The study compares the performance of the long short-term memory (LSTM) model and the GRU model by incrementally extending the prediction period up to 16 weeks. The results demonstrate that the GRU model achieved approximately 70% lower mean squared error (MSE) compared to the LSTM model, achieving superior accuracy even in long-term predictions. Moreover, the GRU model exhibited a smaller MSE increase over extended periods, demonstrating greater stability. These findings affirm the GRU model as an efficient and reliable tool for predicting LNG spot freight rate fluctuations. Our study emphasizes the importance of selecting appropriate models to accurately capture the dynamics of highly volatile markets. The paper provides insights into LNG transportation planning and decision-making.