地政学的ショック下における中国輸出コンテナ運賃指数のレジリエントな予測アプローチ：多因子スクリーニングと多モデル比較に基づく

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Methodological insights on forecasting under non-stationary shocks, applicable to climate-related disruptions.

🏢実務担当者:Logistics and supply chain teams may use the forecasting approach for route adjustment and risk mitigation.

🏛政策担当者:Government agencies could apply the stress-testing framework to geopolitical and potentially climate-related disruptions.

Introduction Geopolitical shocks can cause nonlinear and non-stationary fluctuations in the China Containerized Freight Index (CCFI), posing challenges to conventional forecasting methods. This study evaluates the forecasting resilience of different models under both stable market conditions and periods of extreme volatility. Methods We constructed a system of 18 potential influencing factors covering four dimensions: macroeconomic conditions, supply and demand, cost factors, and market correlations. A multi-stage screening procedure combining Pearson and Spearman correlation analyses, significance testing, and variance inflation factor diagnostics identified eight core factors: the U.S. Industrial Production Index, U.S. Consumer Price Index, Baltic Dry Index, U.S. dollar interest rate, Brent crude oil price, China’s Consumer Price Index, second-hand containership price index, and new containership orders. ARIMA, Random Forest, XGBoost, and long short-term memory (LSTM) models were compared within a unified framework. The 2010–2025 sample was divided into a calm period (2010–2019) and an extreme-volatility period (2020–2025). Results During the calm period, LSTM achieved forecasting performance comparable to that of ARIMA and the tree-based models. During the extreme-volatility period, LSTM recorded an MSE of 45,596.30, an MAE of 180.24, an RMSE of 213.53, and a MAPE of 15.98%, with its R 2 value being the closest to zero among the four models. The Diebold–Mariano tests showed that LSTM significantly outperformed ARIMA during the extreme-volatility period, whereas its advantages over Random Forest and XGBoost were not consistently significant. Discussion LSTM demonstrates greater forecasting resilience than the traditional linear model when CCFI dynamics are disrupted by nonlinear and non-stationary geopolitical shocks. Its gating mechanism may facilitate the representation of temporal dependence and post-shock adjustment. However, its superiority over tree-based machine-learning models is not unconditional and may depend on sample division, feature construction, and hyperparameter settings. These findings provide practical support for conflict-aware route adjustment by shipping companies and geopolitical stress testing by government agencies.

• openalex https://doi.org/10.3389/fmars.2026.1913258first seen 2026-09-09 05:06:12