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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    Titel :

    Forecasting tanker freight rate using neural networks


    Beteiligte:
    Li, Jun (Autor:in) / Parsons, Michael G. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.01.1997




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Forecasting tanker freight rates

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

    Freier Zugriff