Forecasting tanker freight rate using neural networks

Jun Li and Michael G. Parsons

Maritime Policy & Management, 1997, vol. 24, issue 1, 9-30

Abstract: Improvement in forecasting accuracy is a difficult task but critical for business success. This paper investigates the potential of neural networks for short- to long-term prediction of monthly tanker freight rates. Procedures are outlined for the development of the neural networks. The problem of under-training and over-training is addressed by controlling the number of iterations during the training process of neural networks. A comparative study of predictive performance between neural networks and ARMA time series models is conducted. Our evience shows that neural networks can significantly outperform time series models, especially for longer-term forecasting.

Date: 1997
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Citations: View citations in EconPapers (7)

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DOI: 10.1080/03088839700000053

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