A comparative evaluation of machine learning approaches for container freight rates prediction

Abstract

This study evaluates the predictive performance of four models—Decision Tree, Random Forest, Prophet, and LSTM—in forecasting container freight rates, a key metric for strategic decision-making in the shipping industry. To address data heterogeneity, Min-Max normalization was applied, and the Johansen co-integration test confirmed long-term relationships among the variables, justifying the use of raw data in our analysis. Performance was assessed using MSE, RMSE, NMSE, MAE, MAPE and SMAPE. While both Decision Tree and Random Forest models yielded lower absolute errors compared to LSTM and Prophet, the Decision Tree model demonstrated superior relative accuracy, outperforming Random Forest by approximately 91.8 % on the USWC route, 52.1 % on USEC, 43.5 % on MED, and 22.7 % on NEUR. These findings highlight the robustness of the Decision Tree model for container freight rate forecasting under volatile market conditions.