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

Ziaul Haque Munim, Hans-Joachim Schramm

Research output: Contribution to journalJournal articleResearchpeer-review


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
Number of pages19
Publication statusPublished - 2 Apr 2020

Bibliographical note

Epub ahead of print. 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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