MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern
Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8846608 |
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| _version_ | 1849306168427544576 |
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| author | Q. He Y. J. Zheng C.L. Zhang H. Y. Wang |
| author_facet | Q. He Y. J. Zheng C.L. Zhang H. Y. Wang |
| author_sort | Q. He |
| collection | DOAJ |
| description | Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets. |
| format | Article |
| id | doaj-art-388c78e7ea9e44c2939bc888c1f1e22a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-388c78e7ea9e44c2939bc888c1f1e22a2025-08-20T03:55:11ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88466088846608MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature PatternQ. He0Y. J. Zheng1C.L. Zhang2H. Y. Wang3School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaCurrently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. Finally, the model in this paper outperforms other latest models on actual datasets.http://dx.doi.org/10.1155/2020/8846608 |
| spellingShingle | Q. He Y. J. Zheng C.L. Zhang H. Y. Wang MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern Complexity |
| title | MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern |
| title_full | MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern |
| title_fullStr | MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern |
| title_full_unstemmed | MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern |
| title_short | MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern |
| title_sort | mtad tf multivariate time series anomaly detection using the combination of temporal pattern and feature pattern |
| url | http://dx.doi.org/10.1155/2020/8846608 |
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