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Author

Seungwook Park

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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
Open access Sep 2026

From Dot-com to AI: drawdown onsets prediction in AI technology stock market

The study's purpose is to establish whether drawdown-onset prediction with actionable lead time is feasible by combining multiple crisis mechanisms into a unified online probabilistic architecture, rather than relying on the single-mechanism specifications common in the existing literature.

Maksim Kim, Seungwook Park · 0 citations

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