Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329761 
Year of Publication: 
2025
Citation: 
[Journal:] Asian Journal of Shipping and Logistics (AJSL) [ISSN:] 2352-4871 [Volume:] 41 [Issue:] 2 [Year:] 2025 [Pages:] 99-109
Publisher: 
Elsevier, Amsterdam
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.
Subjects: 
Container Freight Rates
Decision Tree
LSTM
Machine learning
Prophet
Random Forest
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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