Review of anomaly detection algorithms for multidimensional time series

doi:10.11772/j.issn.1001-9081.2019101805

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (6): 1553-1564.DOI: 10.11772/j.issn.1001-9081.2019101805

Special Issue: 综述

• Artificial intelligence • Next Articles

Review of anomaly detection algorithms for multidimensional time series

HU Min^1,2, BAI Xue^1,2, XU Wei^1,2, WU Bingjian^1,2

1. SILC Business School, Shanghai University, Shanghai 201800, China
2. SHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, China

Received:2019-10-24 Revised:2019-12-21 Online:2020-06-18 Published:2020-06-10
Contact: BAI Xue, born in 1995, M. S. candidate. Her research interest includes anomaly detection algorithm for multidimensional time series
About author:HU Min, born in 1970, Ph. D., associate professor. Her research interests include intelligent information processing, urban infrastructure construction anomaly detection and prediction.BAI Xue, born in 1995, M. S. candidate. Her research interest includes anomaly detection algorithm for multidimensional time series.XU Wei, born in 1990, M. S. , teaching assistant. His research interests include data mining, intelligent information processing.WU Bingjian，born in 1995, M. S. His research interests include data mining, intelligent control.

多维时间序列异常检测算法综述

胡珉^1,2, 白雪^1,2, 徐伟^1,2, 吴秉键^1,2

1.上海大学悉尼工商学院，上海 201800
2.上海大学—上海城建建筑产业化研究中心，上海 200072

通讯作者: 白雪(1995—)
作者简介:胡珉（1970—），女，浙江上虞人，副教授，博士，主要研究方向：智能信息处理、城市基础设施施工异常检测和预测。白雪(1995—)，女，河南焦作人，硕士研究生，主要研究方向：多维时间序列异常监测算法。徐伟(1990—),男，江苏南通人，助教，硕士，主要研究方向：数据挖掘、智能信息处理。吴秉键（1995—），男，安徽宿州人，硕士，主要研究方向：数据挖掘、智能控制。

Abstract

Abstract:

With the continuous development of information technology, the scale of time series data has grown exponentially, which provides opportunities and challenges for the development of time series anomaly detection algorithm, making the algorithm in this field gradually become a new research hotspot in the field of data analysis. However, the research in this area is still in the initial stage and the research work is not systematic. Therefore, by sorting out and analyzing the domestic and foreign literature, this paper divides the research content of multidimensional time series anomaly detection into three aspects: dimension reduction, time series pattern representation and anomaly pattern detection in logical order, and summarizes the mainstream algorithms to comprehensively show the current research status and characteristics of anomaly detection. On this basis, the research difficulties and trends of multi-dimensional time series anomaly detection algorithms were summarized in order to provide useful reference for related theory and application research.

Key words: multidimensional time series, anomaly detection, dimension reduction, time series pattern representation, anomaly pattern detection

摘要：

随着信息化技术不断提高，时序数据规模呈指数级增长，为时间序列异常检测算法发展提供了契机和挑战，也使其逐步成为数据分析领域新增的研究热点。然而，这一方面的研究仍处于初步阶段，研究工作的系统性不强。为此，通过整理和分析国内外文献，将多维时间序列异常检测的研究内容按照逻辑顺序分为“维数约简”“时间序列模式表示”和“异常模式发现”三个方面，并对其主流算法进行梳理和归纳，以全面展现当前异常检测的研究现状和特点。在此基础上，还指出了多维时间序列异常检测算法的研究难点和研究趋势，以期对相关理论和应用研究提供有益的参考。

关键词: 多维时间序列, 异常检测, 维数约简, 时间序列的模式表示, 异常模式发现

CLC Number:

TP206.3

HU Min, BAI Xue, XU Wei, WU Bingjian. Review of anomaly detection algorithms for multidimensional time series[J]. Journal of Computer Applications, 2020, 40(6): 1553-1564.

胡珉, 白雪, 徐伟, 吴秉键. 多维时间序列异常检测算法综述[J]. 《计算机应用》唯一官方网站, 2020, 40(6): 1553-1564.

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Review of anomaly detection algorithms for multidimensional time series

多维时间序列异常检测算法综述

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