Testing Stationarity with Surrogates — A One-Class SVM Approach

Jun Xiao 1 Pierre Borgnat 1 Patrick Flandrin 1 Cédric Richard 2
1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon
2 ICD - Institut Charles Delaunay
Abstract : An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogates for defining the null hypothesis and to base on them a statistical test implemented as a one-class Support Vector Machine. The time-frequency features extracted from the surrogates are considered as a learning set and used to detect departure from stationnarity. The principle of the method is presented, and some results are shown on typical models of signals that can be thought of as stationary or nonstationary, depending on the observation scale used.
Keywords : Stationarity Test Time-Frequency Analysis Support Vector Machines One-Class Classification
Type de document :
Communication dans un congrès
2007 IEEE/SP 14th Statistical Signal Processing Workshop (SSP '07), Aug 2007, Madison, Wisconsin, United States. IEEE, pp.720-724, 2007
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger
https://hal-ens-lyon.archives-ouvertes.fr/ensl-00175481
Contributeur : Pierre Borgnat <>
Soumis le : vendredi 28 septembre 2007 - 12:50:45
Dernière modification le : jeudi 19 avril 2018 - 14:54:03
Document(s) archivé(s) le : vendredi 9 avril 2010 - 03:04:17

Fichier

ssp07_0000720.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : ensl-00175481, version 1

Collections

ENS-LYON | UNIV-LYON1

Citation

Jun Xiao, Pierre Borgnat, Patrick Flandrin, Cédric Richard. Testing Stationarity with Surrogates — A One-Class SVM Approach. 2007 IEEE/SP 14th Statistical Signal Processing Workshop (SSP '07), Aug 2007, Madison, Wisconsin, United States. IEEE, pp.720-724, 2007. 〈ensl-00175481〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

229

Téléchargements de fichiers

130

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD