Driver estimation in non-linear autoregressive models

Abstract : In non-linear autoregressive models, the time dependency of coefficients is often driven by a particular time-series which is not given and thus has to be estimated from the data. To allow model evaluation on a validation set, we describe a parametric approach for such driver estimation. After estimating the driver as a weighted sum of potential drivers, we use it in a non-linear autoregressive model with a polynomial parametrization. Using gradient descent, we optimize the linear filter extracting the driver, outperforming a typical grid-search on predefined filters.
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Communication dans un congrès
43nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Apr 2018, Calgary, Canada
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Contributeur : Tom Dupré La Tour <>
Soumis le : mardi 30 janvier 2018 - 16:08:19
Dernière modification le : jeudi 21 juin 2018 - 08:42:28
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Tom Dupré La Tour, Yves Grenier, Alexandre Gramfort. Driver estimation in non-linear autoregressive models. 43nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Apr 2018, Calgary, Canada. 〈hal-01696786〉

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