Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process

Given the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during the complex geological mineral grade analysis process, an order self-learning ARHMM (Autoregressive Hidden Markov Model) algorithm is proposed to carry out online outlier de...

Full description

Autores:

Zhao, Jianjun
Zhoub, Junwu
Su, Weixing
Liu, Fang

Tipo de recurso:

Article of journal

Fecha de publicación:

2017

Institución:

Universidad Nacional de Colombia

Repositorio:

Universidad Nacional de Colombia

Idioma:

spa

OAI Identifier:

oai:repositorio.unal.edu.co:unal/63576

Acceso en línea:

https://repositorio.unal.edu.co/handle/unal/63576
http://bdigital.unal.edu.co/64022/

Palabra clave:

55 Ciencias de la tierra / Earth sciences and geology
ARHMM
BDT
KICvc
outlier detection
online detection.
Modelo autoregresivo oculto de Markov
detección en tiempo real
Brockwell-Dahlhaus-Trindade

Rights

openAccess

License

Atribución-NoComercial 4.0 Internacional