Forecasting Period Charter Rates of VLCC Tankers through Neural Networks: A Comparison of Alternative Approaches

Crude oil that is transported by sea represents close to 45% of all internationally traded crude oil and this constitutes the world’s primary source of energy. There is a scarce amount of literature available dealing with the use of artificial neural networks (NNs) in forecasting seaborne transport market rates. This primary focus of this article is on applying NNs to period charter rates forecasting for very large crude oil carriers. The performance achieved for 1- and 3-year period charter rate time series by two different NN models, the multi-layer perceptron and the radial basis function (RBF) is benchmarked against a more elementary performance that is delivered by an autoregressive integrated moving average (ARIMA) model. The authors find that NN modelling delivers encouraging end results that outperform the benchmark model (ARIMA). The authors also point out that NN using RBFs delivers the best overall predictive performance. This article discusses how the usual focus in seaborne freight rate forecasting literature is the spot rate and a limited amount of literature has been directed towards period charter rates.

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  • English

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  • Accession Number: 01519367
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 6 2014 2:39PM