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...
- 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
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| dc.title.spa.fl_str_mv |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| title |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| spellingShingle |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process 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 |
| title_short |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| title_full |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| title_fullStr |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| title_full_unstemmed |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| title_sort |
Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process |
| dc.creator.fl_str_mv |
Zhao, Jianjun Zhoub, Junwu Su, Weixing Liu, Fang |
| dc.contributor.author.spa.fl_str_mv |
Zhao, Jianjun Zhoub, Junwu Su, Weixing Liu, Fang |
| dc.subject.ddc.spa.fl_str_mv |
55 Ciencias de la tierra / Earth sciences and geology |
| topic |
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 |
| dc.subject.proposal.spa.fl_str_mv |
ARHMM BDT KICvc outlier detection online detection. Modelo autoregresivo oculto de Markov detección en tiempo real Brockwell-Dahlhaus-Trindade |
| description |
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 detection in the geological mineral grade analysis process. The algorithm utilizes AR model to fit the time series obtained from “Online x - ray Fluorescent Mineral Analyzer” and makes use of HMM as a basic detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. The structure of traditional BDT (Brockwell-Dahlhaus-Trindade) algorithm is improved to be a double iterative structure in which iterative calculation from both time and order is applied respectively to update parameters of ARHMM online. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of the algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness, and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of mineral grade analysis data in geology and mineral processing. |
| publishDate |
2017 |
| dc.date.issued.spa.fl_str_mv |
2017-07-01 |
| dc.date.accessioned.spa.fl_str_mv |
2019-07-02T21:55:01Z |
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2019-07-02T21:55:01Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
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ISSN: 2339-3459 |
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https://repositorio.unal.edu.co/handle/unal/63576 |
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http://bdigital.unal.edu.co/64022/ |
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ISSN: 2339-3459 |
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https://repositorio.unal.edu.co/handle/unal/63576 http://bdigital.unal.edu.co/64022/ |
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spa |
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spa |
| dc.relation.spa.fl_str_mv |
https://revistas.unal.edu.co/index.php/esrj/article/view/65215 |
| dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research Journal Earth Sciences Research Journal |
| dc.relation.references.spa.fl_str_mv |
Zhao, Jianjun and Zhoub, Junwu and Su, Weixing and Liu, Fang (2017) Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process. Earth Sciences Research Journal, 21 (3). pp. 135-139. ISSN 2339-3459 |
| dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geociencia |
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Zhao, Jianjun966b911f-96e0-4313-a2e4-37b25fc6ca7a300Zhoub, Junwu3d1b2ee0-29cc-4022-b809-7af59a9bc34f300Su, Weixing9deb09aa-cf04-46fd-b53a-ff0a1865ba78300Liu, Fang9ce22b7f-ff3a-4a25-b754-767b9786acc73002019-07-02T21:55:01Z2019-07-02T21:55:01Z2017-07-01ISSN: 2339-3459https://repositorio.unal.edu.co/handle/unal/63576http://bdigital.unal.edu.co/64022/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 detection in the geological mineral grade analysis process. The algorithm utilizes AR model to fit the time series obtained from “Online x - ray Fluorescent Mineral Analyzer” and makes use of HMM as a basic detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. The structure of traditional BDT (Brockwell-Dahlhaus-Trindade) algorithm is improved to be a double iterative structure in which iterative calculation from both time and order is applied respectively to update parameters of ARHMM online. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of the algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness, and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of mineral grade analysis data in geology and mineral processing.Existe gran dificultad para la detección en tiempo real para series de datos masivos con altos niveles de ruido de valores atípicos. Se propone un algoritmo de autoaprendizaje ARHMM (Modelo autoregresivo oculto de Markov) para llevar a cabo la detección de dichos valores atípicos en el proceso de análisis del grado mineral geológico. El algoritmo usa un modelo AR para ajustar la serie de tiempo obtenida del “analizador de fluorescencia de rayos X” y hace uso del HMM como una herramienta básica de detección, la cual puede evitar la deficiencia de predeterminar el umbral en métodos tradicionales de detección. Para actualizar los parámetros del ARHMM en tiempo real, la estructura del algoritmo BDT (Brockwell-Dahlhaus-Trindade) tradicional se mejora para ser una doble estructura iterativa en la que se aplica el cálculo iterativo en tiempo y en orden respectivamente. Con el propósito de reducir la influencia de valores atípicos (o extremos) en la actualización del parámetro de ARHMM, se adoptan las estrategias de detección-antes-que-actualización y la detección-basada-en-actualización, lo que también aumenta la robustez del algoritmo. La subsiguiente simulación por modelos de datos y aplicación práctica comprueba la precisión, fortaleza y capacidad de la detección en línea del algoritmo. De acuerdo con el resultado, es evidente que el nuevo algoritmo propuesto en este artículo es más apropiado para la detección de datos de valores atipicos para el análisis del grado mineral en geología y el procesamiento mineral.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Geocienciahttps://revistas.unal.edu.co/index.php/esrj/article/view/65215Universidad Nacional de Colombia Revistas electrónicas UN Earth Sciences Research JournalEarth Sciences Research JournalZhao, Jianjun and Zhoub, Junwu and Su, Weixing and Liu, Fang (2017) Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process. Earth Sciences Research Journal, 21 (3). pp. 135-139. ISSN 2339-345955 Ciencias de la tierra / Earth sciences and geologyARHMMBDTKICvcoutlier detectiononline detection.Modelo autoregresivo oculto de Markovdetección en tiempo realBrockwell-Dahlhaus-TrindadeOnline Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis ProcessArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL65215-352568-2-PB.pdfapplication/pdf797589https://repositorio.unal.edu.co/bitstream/unal/63576/1/65215-352568-2-PB.pdf45e74bb088c7ab5a9eae5a084cb24266MD51THUMBNAIL65215-352568-2-PB.pdf.jpg65215-352568-2-PB.pdf.jpgGenerated Thumbnailimage/jpeg7622https://repositorio.unal.edu.co/bitstream/unal/63576/2/65215-352568-2-PB.pdf.jpg05d8f4edf0836a0fcee569879d699c56MD52unal/63576oai:repositorio.unal.edu.co:unal/635762024-04-29 23:11:15.859Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |