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.


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    Title :

    Forecasting tanker freight rate using neural networks


    Contributors:

    Published in:

    Publication date :

    1997-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Forecasting tanker freight rates

    Velonias, Platon M. (Platon Michael) | DSpace@MIT | 1995

    Free access