Training and evaluation of neural networks for multi-variate time series processing

Torben L. Fog, Jan Larsen, Lars Kai Hansen

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    We study the training and generalization for multi-variate time series processing. It is suggested to used a quasi-maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the optimal brain damage pruning technique to the multi-variate case. A key ingredient is an algebraic expression for the generalization ability of a multi-variate model. The variability of the suggested techniques are successfully demonstrated in a multi-variate scenario involving the prediction of the cylinder pressure in a marine engine
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Workshop on Neural Networks
    VolumeVolume 2
    Publication date1995
    ISBN (Print)07-80-32768-3
    Publication statusPublished - 1995
    Event1995 IEEE International Conference on Neural Networks - Perth, WA, United States
    Duration: 27 Nov 19951 Dec 1995


    Conference1995 IEEE International Conference on Neural Networks
    Country/TerritoryUnited States
    CityPerth, WA
    Internet address

    Bibliographical note

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