Discontinuous Parsimony Embedding Empowered Transformer for Shipping Market Forecasting

The profitability and survival of ship-owning companies in the global shipping market are deeply intertwined with accurate forecasts of ship prices and charter rates. Effective detection of market shortfalls and capitalization on temporal arbitrage opportunities are essential for maintaining a competitive edge. Traditional forecasting models, while adept at handling various multivariate time series tasks, predominantly focus on embedding synchronous time lags, often neglecting asynchronous dependencies. This paper introduces the Shipping Transformer (SFormer), a novel forecasting model designed to address this gap by integrating a discontinuous and parsimonious embedding strategy. This approach effectively captures lead-lag relationships between explanatory and target series. To further enhance forecasting performance, we introduce a cross-dimension attention module that uncovers cross-series …

Recommended citation: R Gao, Z Wang, M Hu, M Liang, PN Suganthan. (2025). Discontinuous Parsimony Embedding Empowered Transformer for Shipping Market Forecasting. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8. https://ieeexplore.ieee.org/abstract/document/11229012/