Forecasting Container Freight Rates for Major Trade Routes: A Comparison of Artificial Neural Networks and Conventional Models

Ziaul Haque Munim*, Hans-Joachim Schramm

*Corresponding author for this work

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Major players in maritime business such as shipping lines, charterers, shippers, and others rely on container freight rate forecasts for operational decision-making. The absence of a formal forward market in container shipping necessitates reliance on forecasts, also for hedging purposes. To identify better performing forecasting approaches, we compare three models, namely autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) or vector error correction (VEC), and artifcial neural network (ANN) models. We examine the China Containerized Freight Index (CCFI) as a collection of weekly freight rates published by the Shanghai Shipping Exchange (SSE) for four major trade routes. We fnd that, overall, VAR/VEC models outperform ARIMA and ANN in training-sample forecasts, but ARIMA outperforms VAR and ANN taking test-samples. At route level, we observe two exceptions to this. ARIMA performs better for the Far East to Mediterranean route, in the training-sample, and the VEC model does the same in the Far East to US East Coast route in the test-sample. Hence, we advise industry players to use ARIMA for forecasting container freight rates for major trade routes ex-China, except for VEC in the case of the Far East to US East Coast route.
Original languageEnglish
JournalMaritime Economics & Logistics
Issue number2
Pages (from-to)310-327
Number of pages18
Publication statusPublished - Jun 2021

Bibliographical note

Published online: 2. April 2020


  • Artifcial neural networks
  • Vector error correction
  • Forecast performance
  • Ocean freight rates
  • Backpropagation algorithm
  • Diebold-Mariano test

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