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Aug 2026

A Comparative Study of Traditional Econometric, Machine Learning, and Deep Learning Models for Route-Level Forecasting of the Shanghai Containerized Freight Index: Focusing on the US West Coast, US East Coast, and Europe Routes

Using weekly Shanghai Containerized Freight Index (SCFI) data for the US West Coast, US East Coast, and Europe routes (2019–2026; 382 weeks), this study compares traditional econometric methods (ARIMA, SARIMA), machine learning (Random Forest, XGBoost, SVR), and deep learning (LSTM, GRU, Transformer), together with exo...

H. Yoo, S. Kim, Seungwook Park · 0 citations

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