Title :

Nonparametric multiple change point estimation in highly dependent time series

Author(s) :

Khaleghi, Azadeh [Auteur]
Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]

Journal title :

Theoretical Computer Science

Pages :

119-133

Publisher :

Elsevier

Publication date :

2016

ISSN :

1879-2294

HAL domain(s) :

Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Statistiques [math.ST]
Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

Given a heterogeneous time-series sample, the objective is to find points in time, called change points, where the probability distribution generating the data has changed. The data are assumed to have been generated by ...
Show more >Given a heterogeneous time-series sample, the objective is to find points in time, called change points, where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework. The theoretical results are complemented with experimental evaluations.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL