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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1362-1367,1409

Multi-dimensional time series data anomaly detection fusing statistical methods and bidirectional convolutional LSTM

Xia Ying
Han Xingyu
School of Computer Science & Technology, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0451

Abstract

Anomaly detection through data analysis helps to accurately identify abnormal behaviors, improving service quality and decision-making capabilities. However, due to the temporal and spatial dependence of multi-dimensional time series data and the randomness of abnormal events, the existing methods still have certain limitations. Regarding the issue above, this paper proposed a multi-dimensional time series data anomaly detection method MBCLE, which combined new statistical methods and bidirectional convolutional LSTM. The method introduced a stacked median filter to handle point anomalies in the input data and smooth data fluctuations, and designed a neural network predictor combining Bi-ConvLSTM and Bi-LSTM for data modeling and prediction. It smoothed the prediction errors using bidirectional recurrent exponentially weighted moving average(BrEWMA). The method used dynamic threshold to calculate the threshold to detect contextual anomalies. The experimental results show that MBCLE has good detection performance and each step contributes to the performance improvement.

Foundation Support

国家自然科学基金资助项目(41971365)
重庆市高技术产业重大产业技术研发项目(D2018-82)
重庆市教委重点合作项目(HZ2021008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0451
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Algorithm Research & Explore
Pages: 1362-1367,1409
Serial Number: 1001-3695(2022)05-013-1362-06

Publish History

[2021-12-20] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

夏英, 韩星雨. 融合统计方法和双向卷积LSTM的多维时序数据异常检测 [J]. 计算机应用研究, 2022, 39 (5): 1362-1367,1409. (Xia Ying, Han Xingyu. Multi-dimensional time series data anomaly detection fusing statistical methods and bidirectional convolutional LSTM [J]. Application Research of Computers, 2022, 39 (5): 1362-1367,1409. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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