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Conference papers

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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Contributor : Tom Dupré la Tour Connect in order to contact the contributor
Submitted on : Tuesday, January 30, 2018 - 4:08:19 PM
Last modification on : Saturday, June 25, 2022 - 8:28:21 PM
Long-term archiving on: : Friday, May 25, 2018 - 4:43:55 PM


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  • HAL Id : hal-01696786, version 1


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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