Anomaly Detection of Multivariate Time Series Based on Riemannian Manifolds / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 542-547, 2015.
Article
Dans Chinois
| WPRIM
| ID: wpr-359610
ABSTRACT
Multivariate time series problems widely exist in production and life in the society. Anomaly detection has provided people with a lot of valuable information in financial, hydrological, meteorological fields, and the research areas of earthquake, video surveillance, medicine and others. In order to quickly and efficiently find exceptions in time sequence so that it can be presented in front of people in an intuitive way, we in this study combined the Riemannian manifold with statistical process control charts, based on sliding window, with a description of the covariance matrix as the time sequence, to achieve the multivariate time series of anomaly detection and its visualization. We made MA analog data flow and abnormal electrocardiogram data from MIT-BIH as experimental objects, and verified the anomaly detection method. The results showed that the method was reasonable and effective.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Reconnaissance automatique des formes
/
Interprétation d'images assistée par ordinateur
/
Interprétation statistique de données
/
Électrocardiographie
Type d'étude:
Etude diagnostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2015
Type:
Article
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