Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies

This paper proposes a multi-dimensional time series anomaly data detection method based on correlation analysis, to trace the cause of anomaly detection: system failure data and sensor quality problem data are classified, and then real system failures are identified to avoid false detection. Firstly...

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Autor principal:	WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI Jianzhong
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	multi-dimensional time series abnormal detection correlation analysis graph algorithm industrial big data provenance Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/8648b6e8ce5a4934a4f203220c3483d5
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id	oai:doaj.org-article:8648b6e8ce5a4934a4f203220c3483d5
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spelling	oai:doaj.org-article:8648b6e8ce5a4934a4f203220c3483d52021-11-10T08:16:24ZCorrelation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies10.3778/j.issn.1673-9418.20081001673-9418https://doaj.org/article/8648b6e8ce5a4934a4f203220c3483d52021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2953.shtmlhttps://doaj.org/toc/1673-9418This paper proposes a multi-dimensional time series anomaly data detection method based on correlation analysis, to trace the cause of anomaly detection: system failure data and sensor quality problem data are classified, and then real system failures are identified to avoid false detection. Firstly, the time series correlation graph model is proposed, which is further summarized as the time series correlation loop model. The time series correlation set is obtained by extracting the features in the time series correlation cycle, the cause of abnormality is detected, and the system failure is judged according to the result. Through a large number of experiments on real industrial data sets, the effectiveness of the method in the detection of abnormal sources of high-dimensional time series data is verified. Through comparative experiments, it is verified that the method is superior to fundamental algorithms based on statistics and machine learning models in terms of stability and efficiency. The higher dimensionality of time series, the more obvious improvement of the method compared with the fundamental algorithms. This method not only saves the cost, but also realizes the accurate identification of multi-dimensional abnormal data.WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI JianzhongJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlemulti-dimensional time seriesabnormal detectioncorrelation analysisgraph algorithmindustrial big dataprovenanceElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2142-2150 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	multi-dimensional time series abnormal detection correlation analysis graph algorithm industrial big data provenance Electronic computers. Computer science QA75.5-76.95
spellingShingle	multi-dimensional time series abnormal detection correlation analysis graph algorithm industrial big data provenance Electronic computers. Computer science QA75.5-76.95 WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI Jianzhong Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
description	This paper proposes a multi-dimensional time series anomaly data detection method based on correlation analysis, to trace the cause of anomaly detection: system failure data and sensor quality problem data are classified, and then real system failures are identified to avoid false detection. Firstly, the time series correlation graph model is proposed, which is further summarized as the time series correlation loop model. The time series correlation set is obtained by extracting the features in the time series correlation cycle, the cause of abnormality is detected, and the system failure is judged according to the result. Through a large number of experiments on real industrial data sets, the effectiveness of the method in the detection of abnormal sources of high-dimensional time series data is verified. Through comparative experiments, it is verified that the method is superior to fundamental algorithms based on statistics and machine learning models in terms of stability and efficiency. The higher dimensionality of time series, the more obvious improvement of the method compared with the fundamental algorithms. This method not only saves the cost, but also realizes the accurate identification of multi-dimensional abnormal data.
format	article
author	WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI Jianzhong
author_facet	WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI Jianzhong
author_sort	WANG Muxian, DING Xiaoou, WANG Hongzhi+, LI Jianzhong
title	Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
title_short	Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
title_full	Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
title_fullStr	Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
title_full_unstemmed	Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies
title_sort	correlation-based method for tracing multi-dimensional time series data anomalies
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/8648b6e8ce5a4934a4f203220c3483d5
work_keys_str_mv	AT wangmuxiandingxiaoouwanghongzhilijianzhong correlationbasedmethodfortracingmultidimensionaltimeseriesdataanomalies
_version_	1718440393285042176

Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies

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