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Driver estimation in non-linear autoregressive models

Citation Author(s):
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort
Submitted by:
Tom Dupre la Tour
Last updated:
13 April 2018 - 5:21am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Alexandre Gramfort
Paper Code:
1777
 

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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