EnglishIn this paper, the problem of detrending a time series and/or estimating a wandering baseline is addressed. We propose a new methodology that adaptively minimizes different regularized cost functions by introducing an ARMA model of the underlying trend. Mixed ℓ1/ℓ2-norm penalty functions are taken into consideration and novel RLS and LMS solutions are derived for the model parameters estimation. The proposed methods are applied to typical trend estimation/removal problems that can be found in the analysis of economic time series or biomedical signal acquisition. Comparisons with standard noncausal filtering techniques are also presented.
Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling / Giarré, L.; Argenti, F.. - In: JOURNAL OF THE FRANKLIN INSTITUTE. - ISSN 0016-0032. - (2018), pp. 1493-1511.
| Data di pubblicazione: | 2018 |
| Data di prima pubblicazione: | 9-gen-2018 |
| Titolo: | Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling |
| Autore/i: | Giarré, L.; Argenti, F. |
| Autore/i UNIMORE: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.jfranklin.2017.12.009 |
| Rivista: | |
| Pagina iniziale: | 1493 |
| Pagina finale: | 1511 |
| Codice identificativo ISI: | WOS:000425498200024 |
| Codice identificativo Scopus: | 2-s2.0-85040697166 |
| Citazione: | Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling / Giarré, L.; Argenti, F.. - In: JOURNAL OF THE FRANKLIN INSTITUTE. - ISSN 0016-0032. - (2018), pp. 1493-1511. |
| Tipologia | Articolo su rivista |
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