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...
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| Format: | Article |
| Language: | English |
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Universidad Nacional de Colombia
2017-07-01
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| Series: | Earth Sciences Research Journal |
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| Online Access: | https://revistas.unal.edu.co/index.php/esrj/article/view/65215 |
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| author | Jianjun Zhao Junwu Zhoub Weixing Su Fang Liu |
| author_facet | Jianjun Zhao Junwu Zhoub Weixing Su Fang Liu |
| author_sort | Jianjun Zhao |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-4394b8a2ba0d41528610755df1b0f4cf |
| institution | OA Journals |
| issn | 1794-6190 2339-3459 |
| language | English |
| publishDate | 2017-07-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Earth Sciences Research Journal |
| spelling | doaj-art-4394b8a2ba0d41528610755df1b0f4cf2025-08-20T02:19:15ZengUniversidad Nacional de ColombiaEarth Sciences Research Journal1794-61902339-34592017-07-0121313513910.15446/esrj.v21n3.6521546763Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis ProcessJianjun Zhao0Junwu Zhoub1Weixing Su2Fang Liu3School of Information Science & Engineering, Northeastern University, Shenyang 110004, ChinaBeiJing General Research Institute of Mining & Metallurgy, BeiJing 100160, ChinaSchool of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, ChinaSchool of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, ChinaGiven 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.https://revistas.unal.edu.co/index.php/esrj/article/view/65215ARHMMBDTKICvcoutlier detectiononline detection. |
| spellingShingle | Jianjun Zhao Junwu Zhoub Weixing Su Fang Liu Online Outlier Detection for Time-varying Time Series on Improved ARHMM in Geological Mineral Grade Analysis Process Earth Sciences Research Journal ARHMM BDT KICvc outlier detection online detection. |
| title | 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_short | 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 |
| topic | ARHMM BDT KICvc outlier detection online detection. |
| url | https://revistas.unal.edu.co/index.php/esrj/article/view/65215 |
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