A Binary-Classification-Based Metric between ...
Document type :
Article dans une revue scientifique
Title :
A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems
Author(s) :
Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Journal title :
Journal of Machine Learning Research
Pages :
2837-2856
Publisher :
Microtome Publishing
Publication date :
2013
ISSN :
1532-4435
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Informatique [cs]/Théorie de l'information [cs.IT]
English abstract : [en]
A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving ...
Show more >A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addressed. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.Show less >
Show more >A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addressed. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :