Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention

Abstract Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long‐term planning and decision‐making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long‐term prediction. This study proposes a new hybrid framework for the long‐term prediction of the maritime economics index. The framework consists of time‐series decomposition to break down a time‐series into several components (trend, seasonality, and residual), a two‐stage attention mechanism that prioritizes important variables to increase long‐term prediction accuracy and a long short‐term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time‐series methods, including conventional machine learning and deep learning‐based models, in the long‐term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision‐making through accurate long‐term predictions of the maritime economics index.