Abstract: Container shipping serves as a core pillar of global trade, bearing 52% of maritime trade value by worth. However, its freight rates exhibit extreme volatility - the China Containerized Freight Index (CCFI) surged by 215% year-on-year in August 2021, presenting severe challenges for shipping operations and derivative market risk management. Traditional research predominantly relies on macro-level indicators to analyze rate fluctuations but struggles to capture dynamic micro-level market heterogeneity. Although seasonal patterns have been identified by some scholars, their micro-level determinants lack empirical validation. Resolving how to analyze the dual supply-demand driving mechanisms through high-frequency microdata and enhance freight rate prediction accuracy has become an urgent scientific challenge. This study first constructs an operating capacity index using massive AIS data, quantifying actual deployed capacity across shipping routes to precisely map supply-side capacity adjustments and demand-side cargo flow cyclicality at the micro level. Secondly, Seasonal-Trend decomposition using Loess (STL) is employed to decompose operating capacity and freight rates into seasonal, trend, and residual components. Leveraging the economic interpretability of these components, we provide micro-level evidence of freight rate formation mechanisms from supply-demand perspectives. Furthermore, we propose a "decomposition-forecasting" approach. Through empirical comparisons of six capacity models (incorporating operating capacity and its decomposed components) against the ARMA(1,1) benchmark model, we find that models considering only trend components or simultaneously incorporating all components achieve the highest prediction accuracy, with MAPE improving by 7 percentage points over the benchmark. Finally, robustness checks using Empirical Mode Decomposition (EMD) confirm the method's effectiveness, though its weaker seasonal component extraction capability results in 5 percentage point lower accuracy compared to STL. This micro-level evidence confirms that seasonal-incorporated prediction models are more suitable for container freight rate forecasting. The operating capacity index developed in this study addresses the limitations of traditional macro-indicators in capturing micro-market heterogeneity. By revealing dual supply-demand driving mechanisms through decomposition frameworks, it provides micro-empirical support for dynamic pricing models and offers decision-making foundations for shipping companies' capacity allocation and derivative pricing (e.g., freight futures).

Key words: AIS Data, Operating Capacity, Freight Rates, Container Shipping Market, Decomposition-Prediction