A Novel Forecasting System with Data Preprocessing and Machine Learning for Containerized Freight Market

Abstract

The Shanghai Containerized Freight Index (SCFI) and Ningbo Containerized Freight Index (NCFI) serve as crucial indicators for management and decision-making in China’s shipping industry. This study proposes a novel real-time rolling decomposition forecasting system integrating multiple influencing factors. The framework consists of two core modules: data preprocessing and prediction. In the data preprocessing stage, the Hampel filter is utilized to filter and revise each raw containerized freight index dataset, eliminating the adverse effects of outliers. Additionally, variational mode decomposition (VMD) technique is employed to decompose the time series in a rolling manner, effectively avoiding data leakage while extracting significant features. In the forecasting stage, the cheetah optimization algorithm (COA) optimizes the key parameters of the extreme gradient boosting (XGBoost) model, enhancing forecasting accuracy. The empirical analysis based on SCFI and NCFI data reveals that historical pricing serves as a critical determinant, with our integrated model demonstrating superior performance compared to existing methodologies. These findings substantiate the model’s robust generalization capability and operational efficiency across diverse shipping markets, highlighting its potential value for managerial decision-making in maritime industry practices.