A Hybrid Deep Learning and Uncertainty Risk-Aware Forecasting Model for the China Containerized Freight Market

The China Containerized Freight Index exhibits multi-scale periodicity and nonlinear responses to uncertainty, which challenge traditional forecasting methods. This study proposes a dynamic multi-stage deep learning framework with COVID-19 as an interval node to construct event windows. Breakpoint detection identifies shipping-related events. A three-stage procedure, including Maximal Information Coefficient, Boruta, and Granger causality, selects uncertainty risk indicators as core features, while K-shape clustering groups the exogenous variables. The proposed hybrid model integrates a Temporal Convolution Kolmogorov–Arnold Network with a Warped Fourier and Shock Kernel. Prophet decomposition supplies baseline and residual terms. Temporal Convolution Kolmogorov–Arnold Network unifies local temporal feature extraction and universal nonlinear approximation under sparse samples. The Warped Fourier component adapts to drifting and superimposed seasonality, and the Shock Kernel quantifies uncertainty shock intensity and decay. A gating fusion mechanism suppresses noise and enhances information efficiency. Comparative experiments demonstrate competitive accuracy and robustness, with statistically significant gains in several benchmark comparisons; ablation studies confirm incremental contributions of each component. Empirical analysis shows that under event-driven uncertainty, demand-side policy variables show stronger predictive relevance to China Containerized Freight Index fluctuations, while simultaneously transmitting effects to the carbon market and accelerating the green energy cost transition. These findings provide insights for freight rate forecasting and shipping market risk management.