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References from A resilient forecasting approach of China’s export container freight index under geopolitical shocks based on a multi-factor screening and multi-model comparison. Local targets link to admitted publications; unresolved targets remain external evidence.
A survey of shipping finance research: Setting the future research agenda
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An econometric model of the world tanker market
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Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on emd, grey wave and arma
10.1108/gs-05-2020-0069 · 2021 · External reference
Exchange rate and transport cost sensitivities of bilateral freight flows between the us and China
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The effects of geopolitical risk and economic policy uncertainty on dry bulk shipping freight rates
10.1080/00036846.2020.1857329 · 2021 · External reference
Dynamics of the co-movement between stock and maritime markets
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Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm
10.1057/mel.2016.1 · 2017 · External reference
Forecasting baltic dirty tanker index by applying wavelet neural networks
10.4236/jtts.2013.31008 · 2013 · External reference
Distributionally robust optimization of sailing speed, bunkering, and fuel switching for dual-fuel liner services
10.1016/j.trc.2026.105528 · 2026 · External reference
Forecasting shanghai container freight index: A deep-learning-based model experiment
10.3390/jmse10050593 · 2022 · External reference
Return lead–lag and volatility transmission in shipping freight markets
10.1080/03088839.2013.865849 · 2014 · External reference
The study on correlation between the shipbuilding order quantity of major shipping liners and maritime freight rates: Using granger causality analysis
10.37059/tjosal.2016.32.1.5 · 2016 · External reference
Maritime shipping and export trade on “maritime silk road
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A tradable carbon credit incentive scheme based on the public-private-partnership
10.1016/j.tre.2025.104039 · 2025 · External reference
A comparative evaluation of machine learning approaches for container freight rates prediction
10.1016/j.ajsl.2025.05.001 · 2025 · External reference
State-space tbats model for container freight rate forecasting with improved accuracy
10.1016/j.martra.2022.100057 · 2022 · External reference
Predicting the costs of forwarding contracts using xgboost and a deep neural network
2022 · External reference
A simulation-driven hybrid spn–machine learning framework for container freight rate forecasting under uncertainty
10.21203/rs.3.rs-8587911/v1 · 2026 · External reference
Unresolved reference
2025 · External reference
A novel approach to forecasting the bulk freight market
10.1016/j.ajsl.2017.03.005 · 2017 · External reference
Analysis of influencing factors and prediction of China’s containerized freight index
10.3389/fmars.2023.1245542 · 2023 · External reference
A co-integration approach to forecasting freight rates in the dry bulk shipping sector
10.1016/s0965-8564(97)00002-5 · 1997 · External reference
An interpretable multi-model ensemble deep learning framework for forecasting ocean freight indices with external uncertainty factors: W. wang et al
10.1057/s41278-025-00321-8 · 2025 · External reference
A hybrid lstm-based ensemble learning approach for China coastal bulk coal freight index prediction
10.1155/2021/5573650 · 2021 · External reference
A feature selection-enhanced machine learning framework for clean-energy shipbuilding predictions
10.1016/j.ocecoaman.2026.108175 · 2026 · External reference
How to support shore-to-ship electricity constructions: Tradeoff between government subsidy and port competition
10.1016/j.tre.2025.104258 · 2025 · External reference
Who predicts better? a comparison of machine learning and econometrics in forecasting co2 emissions from global shipping
10.1016/j.energy.2025.138967 · 2025 · External reference
Seasonality patterns in the container shipping freight rate market
10.1080/03088839.2017.1420260 · 2018 · External reference
A novel hybrid approach to baltic dry index forecasting based on a combined dynamic fluctuation network and artificial intelligence method
10.1016/j.amc.2019.05.043 · 2019 · External reference
Large containership stowage planning for maritime logistics: A novel meta-heuristic algorithm to reduce the number of shifts
10.1016/j.aei.2024.102962 · 2025 · External reference
Joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand in maritime logistics industry
10.1016/j.tre.2025.104437 · 2025 · External reference