Extraction of the relevant delays for temporal modeling

Cyril Goutte

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    Abstract

    When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model. This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.
    Original languageEnglish
    JournalI E E E Transactions on Signal Processing
    Volume48
    Issue number6
    Pages (from-to)1787-1795
    ISSN1053-587X
    DOIs
    Publication statusPublished - 2000

    Bibliographical note

    Copyright: 2000 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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