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

Torben L. Fog, Jan Larsen, Lars Kai Hansen

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    Abstract

    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
    PublisherIEEE
    Publication date1995
    Pages1194-1199
    ISBN (Print)07-80-32768-3
    DOIs
    Publication statusPublished - 1995
    Event1995 IEEE International Conference on Neural Networks - Perth, WA, United States
    Duration: 27 Nov 19951 Dec 1995
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=3505

    Conference

    Conference1995 IEEE International Conference on Neural Networks
    Country/TerritoryUnited States
    CityPerth, WA
    Period27/11/199501/12/1995
    Internet address

    Bibliographical note

    Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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