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Driver estimation in non-linear autoregressive models
![](https://sigport.org/sites/default/files/styles/home/public/ECoG_sym%3D1_2017-10-24-21h03_1.png?itok=tOciA_kk)
- Citation Author(s):
- 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
- Categories:
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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.