Prediction of iron ore inventory at ports: A decomposition-integration hybrid approach incorporating key influencing factors

Abstract

An accurate prediction of iron ore inventory at ports is necessary for analyzing market trends, optimizing operational strategies, and avoiding supply chain risks. Considering that the iron ore inventory is complex and influenced by various factors, this study offers a novel decomposition-integration hybrid model to fully capture the underlying patterns in inventory data and improve prediction accuracy. First, three significant components are extracted from the raw inventory sequence to represent high-, mid-, and low-frequency features using CEEMDAN decomposition and sample entropy reconstruction. Then, after investigating the potential influencing factors and diverse characteristics of each frequency sequence, we individually develop the prediction models by incorporating different influencing factors. Finally, the individual models’ outputs are integrated to achieve the final prediction, fully capturing the impact of key influencing factors on the iron ore inventory data at ports. Empirical results based on the data from Qingdao Port illustrate that the established hybrid forecasting model yields ideal accuracy, with at least a 2.11% reduction in RMSE and a minimum 1.73% reduction in MAE compared with nine models, verifying its effectiveness in forecasting iron ore inventory at ports.