Recherche
Panier électronique
Votre panier ne contient aucune notice
Connexion à la base
(Identifiez-vous pour accéder aux fonctions de mise à jour. Utilisez votre login-password de courrier électronique)
Entrepôt OAI-PMH
 | Consulter la notice détaillée |
 | Version complète en ligne |
 | Version complète en ligne accessible uniquement depuis l'Ircam |
 | Ajouter la notice au panier |
 | Retirer la notice du panier |
(full translation not yet available)
Consultation des notices
| Vue détaillée | Vue Refer | Vue Labintel | Vue BibTeX |
|
%0 Journal Article %A Esling, Philippe %A Agon, Carlos %T Time Series Data Mining %D 2011 %B ACM Computing Surveys %F Esling11a %K Time series %K data mining %K distance measures %K sequence matching %K query by content %K sound databases %X In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this paper we intend to provide a survey of the techniques applied for time series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submit various research trends and avenues that can be explored in the near future. We hope that this paper can provide a broad and deep understanding of the time series data mining research field. %1 1 %2 2 |