多维时间序列异常检测算法综述

doi:10.11772/j.issn.1001-9081.2019101805

《计算机应用》唯一官方网站 ›› 2020, Vol. 40 ›› Issue (6): 1553-1564.DOI: 10.11772/j.issn.1001-9081.2019101805

所属专题：综述

• 人工智能 • 下一篇

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

收稿日期:2019-10-24 修回日期:2019-12-21 发布日期:2020-06-18 出版日期:2020-06-10
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。

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.

摘要/Abstract

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

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

中图分类号:

TP206.3

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, 2020, 40(6): 1553-1564.

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.

参考文献

1 肖辉.时间序列的相似性查询与异常检测[D].上海：复旦大学,2005：1,12-30. XIAOH. Similarity query and anomaly detection of time series [D]. Shanghai: Fudan University,2005：1,12-30.
2 苏雅茹.高维数据的维数约简算法研究[D].合肥：中国科技大学,2012：6. SUNY R. Study on dimension reduction algorithm of high dimensional data[D]. Hefei: University of Science and Technology of China, 2012：6.
3 HAWKINSD M. Identification of Outliers [M]. Pretoria， South Africa： Council for Scientific and Industrial Research, 1980：1.
4 杨风召.高维数据挖掘中若干关键问题的研究[D].上海：复旦大学,2003：12-15. YANGF Z. Research on some key problems in high dimensional data mining [D]. Shanghai: Fudan University, 2003：12-15.
5 BLUMA L, LANGLEYP. Selection of relevant features and examples in machine learning [J]. Artificial Intelligence, 1997, 97(1/2): 245-271.
6 YANGK, SHAHABIC. A PCA-based similarity measure for multivariate time series [C]// Proceedings of the 2nd ACM International Workshop on Multimedia Databases. New York: ACM, 2004: 65-74.
7 郭小芳,李锋.基于Eros的多元时间序列相似度分析[J].计算机工程与应用,2012,48(23):111-115. GUOX F, LIF. Similarity analysis of multiple time series based on Eros[J]. Computer Engineering and Applications, 2012, 48(23): 111-115.
8 郭小芳，李锋，宋晓宁.一种基于PCA的时间序列异常检测方法[J].江西师范大学学报(自然科学版),2012,36(3):28-283. GUOX F, LIF, SONGX N. A time series anomaly detection method based on PCA[J]. Journal of Jiangxi Normal University (Natural Science Edition), 2012, 36(3): 28-283.
9 席剑辉,韩敏.主成分分析与神经网络的结合在多变量序列预测中的应用[J].控制理论与应用,2007,24(5):719-724. XIJ H, HANM. The application of principal component analysis and neural network in multivariable sequence prediction [J]. Control Theory and Application, 2007, 24(5): 719-724.
10 BRAUCKHOFFD, SALAMATIANK, MAY M. Applying PCA for traffic anomaly detection: problems and solutions [C]// Proceeding of the 2009 INFOCOM. Piscataway: IEEE, 2009: 46-53.
11 RINGBERGH, SOULEA, REXFORDJ, et al. Sensitivity of PCA for traffic anomaly detection[J]. Performance Evaluation Review, 2007, 35(1): 109-120.
12 钱叶魁,陈鸣,叶立新,等.基于多尺度主成分分析的全网络异常检测方法[J].软件学报,2012,23(2):361-377. QIANY K, CHENM, YEL X, et al. A multi-scale PCA based anomaly detection method for the whole network [J]. Journal of Software, 2012, 23(2) :361-377.
13 CAMACHOJ, PEREZ-VILLEGASA, GARCIA-TEDOROP, et al. PCA-based multivariate statistical network monitoring for anomaly detection[J].Computers & Security,2016,59:118-137.
14 PAFFENROTHR, KAY K, SERVIL. Robust PCA for anomaly detection in cyber networks [EB/OL]. [2019-01-04]. http://xueshu.baidu.com/usercenter/paper/show?paperid=8368c46755840 1a41852f6111551fe31&site=xueshu_se&hitarticle=1&sc_from=shu.
15 ROWEISS T, SAULL K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
16 TENENBAUMJ B.A global geometric framework for nonlinear dimensionality reduction [J]. Science, 2000, 290(5500):2319-2323.
17 徐蓉，姜峰，姚鸿勋.流形学习概述[J].智能系统学报,2006，1(1)：44-51. XUR, JIANGF, YAOH X. Overview of manifold learning[J]. Journal of Intelligent Systems, 2006，1(1)：44-51.
18 马瑞，王家威，宋亦旭.基于局部线性嵌入(LLE)非线性降维的多流形学习[J].清华大学学报(自然科学版),2008,48(4):583-586. MAR, WANGJ W, SONGY X. Poly-manifold learning based on Locally Linear Embedding (LLE) nonlinear dimensionality reduction [J]. Journal of Tsinghua University (Natural Science), 2008,48(4):583-586.
19 李波.基于流形学习的特征提取方法及其应用研究[D].合肥：中国科学技术大学,2008：1-125. LIB. Study on feature extraction based on manifold learning and its application[D]. Hefei: University of Science and Technology of China,2008： 1-125.
20 管河山，姜青山，王声瑞.基于点分布特征的多元时间序列模式匹配方法[J].软件学报,2009,20(1):67-79. GUANH S, JIANGQ S, WANGS R. Multiple time series pattern matching method based on point distribution characteristics [J]. Journal of Software, 2009, 20(1): 67-79.
21 李晓菲,梁循,周小平.股价时间序列滑动窗口的流形学习实证研究[J].中国管理科学,2016，24(S1):495-503. LIX F, LIANGX, ZHOUX P. An empirical study on manifold learning of stock price time series sliding window [J]. Chinese Management Science, 2016，24(S1): 495-503.
22 黄焱.基于流形学习的金融数据分析方法研究[D].成都：电子科技大学,2015：32-44. HUANGY. Study on financial data analysis method based on manifold learning [D]. Chengdu: University of Electronic Science and Technology of China, 2015：32-44.
23 周鑫.时间序列异常模式挖掘关键技术研究[D].西安：西安科技大学, 2011：32-51. ZHOUX. Research on key technology of time series anomaly pattern mining [D]. Xi’an: Xi’an University of Science and Technology, 2011：32-51.
24 邵年华,沈冰,黄领梅,等.KPCA_SVM水文时间序列预测模型的建立与应用[J].西北农林科技大学学报(自然科学版), 2009, 37(9):204-208. SHAON H, SHENB, HUANGL M, et al. Establishment and application of hydrological time series prediction model based on KPCA_SVM [J]. Journal of Northwest Agricultural and Forestry University of Science and Technology (Natural Science Edition), 2009, 37(9): 204-208.
25 JENKINSO C, MATARICM J. A spatio-temporal extension to Isomap nonlinear dimension reduction[C]// Proceedings of the Twenty-First International Conference on Machine Learning. New York: ACM, 2004: 441-448.
26 李权,周兴社.基于KPCA的多变量时间序列数据异常检测方法研究[J].计算机测量与控制,2011,19(4):822-825. LIQ, ZHOUX S. Study on multivariate time series data anomaly detection method based on KPCA [J]. Computer Measurement and Control, 2011, 19(4): 822-825.
27 JIDIGAG R, SAMMULALP. Anomaly detection through comparison of heterogeneous machine learning classifiers vs KPCA [C]// Proceedings of the 2015 International Symposium on Security in Computing and Communication, CCIS 536. Cham: Springer, 2015: 483-495.
28 NAVIM, MESKINN, DAVOODIM. Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA[J]. Journal of Process Control, 2018, 64(21): 37-48.
29 DUANY, ZHAOY P, XYU Y Q, et al. Unmanned aerial vehicle sensor data anomaly detection using kernel principle component analysis [C]// Proceedings of the 2018 IEEE International Conference on Electronic Measurement & Instruments. Piscataway: IEEE, 2018: 241-246.
30 石浩.基于等距特征映射的非线性降维及其应用研究[D].合肥：中国科技大学，2017：6，33-52. SHIH. Nonlinear dimensionality reduction based on ISOMAP and its applications[D]. Hefei: University of Science and Technology of China, 2017：6，33-52.
31 DE SILVAV, TENENBAUMJ B. Sparse multidimensional scaling using landmark points [R]. Palo Alto:Stanford University, 2004: 1.
32 ORSENIGOC, VERCELLISC. Dimensionality reduction via ISOMAP with lock-step and elastic measures for time series gene expression classification[C]// Proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, LNCS 7833. Berlin: Springer, 2013: 92-103.
33 DADKHAHIH, DUARTEM F, MARLINB. ISOMAP out-of-sample extension for noisy time series data [C]// Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing. Piscataway: IEEE, 2015: 1-6.
34 ZHOUY, QYU J, CHAIYI, et al. Sensor fault diagnosis method based on Hilbert marginal spectrum and supervised locally linear embedding and support vector machine [C]// Proceedings of the 2017 Chinese Automation Congress. Piscataway: IEEE, 2017: 546-551.
35 WUQ, QIZ, WANGZ, et al. An improved weighted local linear embedding algorithm[C]// Proceedings of the 2018 14th International Conference on Computational Intelligence and Security. Washington, DC: IEEE Computer Society, 2018: 378-381.
36 LIM-A, XIH-W, SUNY-J. Feature extraction and visualization of MI-EEG with L-MVU algorithm [C]// Proceedings of the 2018 World Congress on Medical Physics and Biomedical Engineering, IFMBE 68/3. SpringerSingapore:, 2019: 835-839.
37 LENSENA, XUEB, ZHANGM. Can genetic programming do manifold learning too? [EB/OL]. [2019-02-08]. https://arxiv.org/abs/1902. 02949?context=cs.
38 田政雄.基于小波变换的时间序列挖掘研究[D].天津：天津大学,2008：11-14. TIANZ X. Time series mining based on wavelet transform [D]. Tianjin: Tianjin University, 2008：11-14.
39 ASTROMK J. On the choice of sampling rates in parametric identification of time series[J]. Information Sciences, 1969, 1(3): 273-278.
40 KEOGHE J, PAZZANIM J. A simple dimensionality reduction technique for fast similarity search in large time series databases[C]// Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 1805. Berlin: Springer, 2000: 122-133.
41 HUNGN Q V, ANH D T. An improvement of PAA for dimensionality reduction in large time series databases [C]// Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence, LNCS 5351. Berlin: Springer, 2008: 698-707.
42 ZHANGY, GALLIMOREM, BINGHAMC, et al. Hybrid hierarchical clustering-piecewise aggregate approximation, with applications[J]. International Journal of Computational Intelligence and Applications, 2016, 15(4): 164-181.
43 RENH, LIUM, LIZ, et al. A piecewise aggregate pattern representation approach for anomaly detection in time series [J]. Knowledge-Based Systems, 2017, 135: 29-39.
44 ZHANGC, CHENY, YINA, et al. An improvement of PAA on trend-based approximation for time series [C]// Proceedings of the 2018 18th International Conference on Algorithms and Architectures for Parallel Processing. Berlin: Springer, 2018: 248-262.
45 PAVLIDIST, HORWITZS L. Segmentation of plane curves [J]. IEEE Transactions on Computers, 1974, 23(8): 860-870.
46 KEOGHE, CHAKRABARTIK, PAZZANIM, et al. Dimensionality reduction for fast similarity search in large time series databases [J]. Knowledge Information System, 2001, 3(3): 263-286.
47 PRATK B, FINKE. Search for patterns in compressed time series[J]. International Journal of Image and Graphics, 2002, 2(1): 89-106.
48 XIAOH, FENGX-F, HUY-F. A new segmented time warping distance for data mining in time series database [C]// Proceeding of the 2004 International Conference on Machine Learning and Cybernetics. Piscataway: IEEE, 2004: 1277-1281.
49 詹艳艳,徐荣聪,陈晓云.基于插值边缘算子的时间序列模式表示[J].模式识别与人工智能,2007，20(3):421-427. ZHANY Y, XUR C, CHENX Y. Time series pattern representation based on interpolation edge operator [J]. Pattern Recognition and Artificial Intelligence, 2007， 20(3): 421-427.
50 赵建秀,王洪国,邵增珍,等.一种基于信息熵的时间序列分段线性表示方法[J].计算机应用研究,2013,30(8):2391-2394. ZHAOJ X, WANGH G, SHAOZ Z， et al. A piecewise linear representation of time series based on information entropy [J]. Application Research of Computers , 2013, 30(8): 2391-2394.
51 WANGJ, YUANH, WUQ, et al. A piecewise linear representation based on compression ratio [C]// Proceedings of the 2015 Prognostics & System Health Management Conference. Piscataway: IEEE, 2015: 1-5.
52 JIC, LIUS, YANGC, et al. A piecewise linear representation method based on importance data points for time series data [C]// Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design. Piscataway: IEEE, 2016: 111-116.
53 孙志伟,董亮亮,马永军.一种基于重要点的时间序列分段算法[J].计算机工程与应用,2018,54(18):250-255. SUNZ Y, DONGL L ,MAY J. A time series segmentation algorithm based on important points [J]. Computer Engineering and Applications, 2018, 54(18): 250-255.
54 LINJ, KEOGHE, LONARDIS. A symbolic representation of time series, with implications for streaming algorithms [C]// Proceedings of the Eighth ACM SIGMOD International Conference on Management of Data Workshop on Research Issues in Data Mining and Knowledge Discovery. New York: ACM, 2003: 2-11.
55 KEIICHIT, TATSUHIROS, TAKUMII. Time series classification using MACD-histogram-based SAX and its performance evaluation [C]// Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway: IEEE, 2016: 2419-2424.
56 WANGZ, OATEST. Pooling SAX-BoP approaches with boosting to classify multivariate synchronous physiological time series data[C]// Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference. Menlo Park: AAAI Press, 2015: 335-340.
57 SUNY, LIJ, LIUJ, et al. An improvement of symbolic aggregate approximation distance measure for time series [J]. Neurocomputing, 2014, 138(11): 189-198.
58 ZANC T, YAMANAH. An improved symbolic aggregate approximation distance measure based on its statistical features [C]// Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services. New York: ACM, 2016: 72-80.
59 ZHANP, HUY, ZHANGQ, et al. Feature-based dividing symbolic time series representation for streaming data processing [C]// Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education. Washington, DC: IEEE Computer Society, 2018: 817-823.
60 ZHANGK, LIY, CHAIY, et al. Trend-based symbolic aggregate approximation for time series representation [C]// Proceedings of the 30th Chinese Control and Decision Conference. Piscataway: IEEE, 2018: 2234-2240.
61 吕玉红.时间序列异常检测算法的研究与应用[D].成都：电子科技大学,2018：24-57. LYU Y H. Research and application of time series anomaly detection algorithm [D]. Chengdu: University of Electronic Science and Technology of China, 2018： 24-57.
62 AGRAWALR, FALOUTSOSC, SWAMIA, et al. Efficient similarity search in sequence databases [C]// Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, LNCS 730. Berlin: Springer, 1993: 69-84.
63 GABORD. Theory of communication [J]. Journal of Institute of Electrical Engineers, 1996, 93(26): 429-459.
64 范立朋.斜拉桥健康监测数据特征分析与重车荷载识别[D].上海：华东理工大学，2016：23-26. FANL P. Characteristics analysis of health monitoring data of cable stayed bridge and recognition of heavy vehicle load [D]. Shanghai: East China University of Science and Technology, 2016： 23-26.
65 CHANK, FUW. Efficient time series matching by wavelets[C]// Proceedings of the 15th International Conference on Data Engineering. Washington, DC: IEEE Computer Society, 1999: 126-133.
66 TSENGV S, CHENC H, HUANGP C, et al. Cluster-based genetic segmentation of time series with DWT [J]. Pattern Recognition Letters, 2009, 30(13): 1190-1197.
67 CHENY, ZHAOQ, HUB, et al. A method of removing ocular artifacts from EEG using discrete wavelet transform and Kalman filtering [C]// Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine. Piscataway: IEEE, 2016: 1485-1492.
68 KHOKHARS, ZIN A A M , MEMONA P , et al. A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network [J]. Measurement, 2017, 95: 246-259.
69 贾瑞.多维时间序列数据挖掘技术研究[D].南京：南京航空航天大学，2008：1-52. JIAR. Research on multidimensional time series data mining technology [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008： 1-52.
70 BOX G E, JENKINSG M. Time series analysis: forecasting and control rev. ed.[J]. Journal of Marketing Research, 1976, 31(4): 238-242.
71 黄文杰,曹鸿兴,顾岚,等.时间序列的ARIMA季节模型在长期预报中的应用[J].科学通报,1980,25(22):1030-1032. HUANGW J, CAOH X, GUL, et al. Application of ARIMA seasonal model of time series in long-term forecasting [J]. Chinese Science Bulletin, 1980, 25(22): 1030-1032.
72 冯丹,韩晓娜,赵文娟,等.中国内地法定报告传染病预测和监测的ARIMA模型[J].疾病控制杂志,2007,11(2):140-143. FENGD, HANX N, ZHAOW J, et al. ARIMA model for the prediction and surveillance of statutory infectious diseases in mainland China [J]. Journal of Disease Control, 2007, 11(2): 140-143.
73 罗静,杨书,张强,等.时间序列ARIMA模型在艾滋病疫情预测中的应用[J].重庆医学,2012,41(13):1255-1256. LUOJ, YANGS, ZHANGQ, et al. Application of time series ARIMA model in AIDS epidemic prediction [J]. Chongqing Medical Science, 2012, 41(13): 1255-1256.
74 涂小萌,陈强国.基于ARIMA-LSSVM混合模型的犯罪时间序列预测[J].电子技术应用,2015,41(2):160-162. TUX M, CHENG Q. Prediction of crime time series based on ARIMA-LSSVM hybrid model [J]. Application of Electronic Technology, 2015, 41(2): 160-162.
75 KHANDELWALI, SATIJAU, ADHIKARIR. Efficient financial time series forecasting model using DWT decomposition [C]// Proceedings of the 2015 IEEE International Conference on Electronics, Computing and Communication Technologies. Piscataway: IEEE, 2015: 1-5.
76 陈兴蜀,江天宇,曾雪梅,等.基于多维时间序列分析的网络异常检测[J].工程科学与技术,2017,49(1):144-150. CHENX S, JIANGT Y, ZENGX M, et al. Network anomaly detection based on multi-dimensional time series analysis [J]. Engineering Science and Technology，2017,49(1): 144-150.
77 ZENGJ, ZHANGL, SHIG, et al. An ARIMA based real-time monitoring and warning algorithm for the anomaly detection [C]// Proceedings of the 2017 IEEE 23rd International Conference on Parallel and Distributed Systems. Piscataway: IEEE, 2017: 469-476.
78 MADANR, SARATHIMANGIPUDIP. Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN [C]// Proceedings of the IEEE 2018 Eleventh International Conference on Contemporary Computing. Piscataway: IEEE, 2018: 1-5.
79 刘春红,杨亮,邓河,等.基于ARIMA和BP神经网络的猪舍氨气浓度预测[J].中国环境科学,2019,39(6):2320-2327. LIUC H, YANGL, DENGH, et al. Prediction of ammonia concentration in pigsty based on ARIMA and BP neural network [J]. Environmental Science in China, 2019, 39(6): 2320-2327.
80 BAUML E, PETRIET, SOULESG, et al. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains [J]. The Annals of Mathematical Statistics, 1970, 41(1): 164-171.
81 QIAOY, XINX W, BIN Y, et al. Anomaly intrusion detection method based on HMM [J]. Electronics Letters, 2002, 38(13): 663-664.
82 李和平,胡占义，吴毅红，等.基于半监督学习的行为建模与异常检测[J].软件学报,2007,18(3):527-537. LIH P, HUZ Y, WUY H, et al. Behavior modeling and anomaly detection based on semi-supervised learning [J]. Journal of Software, 2007, 18(3): 527-537.
83 HOANGX A, HUJ. An efficient hidden Markov model training scheme for anomaly intrusion detection of server applications based on system calls[C]// Proceeding of the 2004 12th IEEE International Conference on Networks. Piscataway: IEEE, 2004: 470-474.
84 孙美凤，黄飞，陈云著，等.基于特征模式的马尔可夫链异常检测模型[J].计算机工程,2008,32(24):155-157. SUNM F, HUANGF, CHENY Z, et al. Markov chain anomaly detection model based on feature pattern[J]. Computer Engineering, 2008, 32(24): 155-157.
85 王琼，倪桂强，潘志松，等.基于改进隐马尔可夫模型的系统调用异常检测[J].数据采集与处理，2009,24(4):508-513. WANGQ, NIG Q, PANZ S, et al. System call anomaly detection based on improved hidden Markov model [J]. Journal of Data Acquisition and Processing, 2009, 24(4): 508-513.
86 NIINAG, DOUZONOH, ENDAT, et al. HMM training by using a self-organizing map for time series prediction [C]// Proceedings of the 2017 International Conference on Machine Learning & Computing. New York: ACM, 2017: 147-152.
87 BHUSARIV, PATILS. Study of hidden Markov model in credit card fraudulent detection [J]. International Journal of Computer Applications, 2011, 20(5): 33-36.
88 GUANX, RAICHR, WONGW K. Efficient multi-instance learning for activity recognition from time series data using an auto-regressive hidden Markov model [C]// Proceedings of the 2016 International Conference on International Conference on Machine Learning. New York: ACM, 2016: 2330-2339.
89 KABIRM H, HOQUEM R, THAPAK, et al. Two-layer hidden Markov model for human activity recognition in home environments[J]. International Journal of Distributed Sensor Networks, 2016, 2016: Article No. 15.
90 ZHENGH, WANGR Y, WANGY F, et al. Fault diagnosis of photovoltaic inverters using hidden Markov model [C]// Proceedings of the 36th Chinese Control Conference. Piscataway: IEEE, 2017: 7290-7295.
91 FORRESTS, PERELSONA S, ALLENL, et al. Self-nonself discrimination in a computer [C]// Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy. Piscataway: IEEE, 1994: 202-212.
92 GONZALEZF A. A study of artificial immune systems applied to anomaly detection [D]. Memphis: University of Memphis, 2003：32-148.
93 董永贵,孙照焱,贾惠波.时间序列中异常值检测的负向选择算法[J].机械工程学报,2004,40(10):30-34. DONGY G, SUNZ Y, JIAH B. Negative selection algorithm for outliers detection in time series [J]. Chinese Journal of Mechanical Engineering, 2004, 40(10): 30-34.
94 汪慧敏,高晓智，黄显林,等.基于改进负选择算法的异常检测[J].计算机仿真,2008,25(5):334-338. WANGH M, GAOX Z, HUANGX L, et al. Anomaly detection based on improved negative selection algorithm [J]. Computer Simulation，2008, 25(5): 334-338.
95 GONGM, ZHANGJ, MAJ, et al. An efficient negative selection algorithm with further training for anomaly detection [J]. Knowledge-Based Systems, 2012, 30(2): 185-191.
96 WENC, DINGX M, LIT, et al. Negative selection algorithm based on grid file of the feature space [J]. Knowledge-Based Systems, 2014, 56(C): 26–35.
97 IDRISI, SELAMATA, NGUYENN T, et al. A combined negative selection algorithm — particle swarm optimization for an email spam detection system [J]. Engineering Applications of Artificial Intelligence, 2015, 39: 33-44.
98 LID, LIUS L, ZHANGH L. A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection [J]. Engineering Applications of Artificial Intelligence, 2016, 50(C): 93-105.
99 MACQUEENJ B. Some methods for classification and analysis of multivariate observations [C]// Proceeding of 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley:AMS,1967: 281-297.
100 TZORTZISG, LIKASA. The minmax K-means clustering algorithm[J]. Pattern Recognition,2011,44(4):866-876.
101 HANZ J. An adaptive K-means initialization method based on data density [J]. Computer Applications and Software, 2014,31(2):182-187.
102 左进,陈泽茂.基于改进K均值聚类的异常检测算法[J].计算机科学,2016,43(8):258-261. ZUOJ, CHENZ M. Anomaly detection algorithm based on improved K-means clustering [J]. Computer Science, 2016,43(8):258-261.
103 ZHANGT F , MAF M. Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function[J]. International Journal of Computer Mathematics, 2017, 94(4):663-675.
104 翁小清,沈钧毅.多变量时间序列例外模式的识别[J].模式识别与人工智能,2007,20(3):336-342. WENGX Q, SHENJ Y. Recognition of exception patterns in multivariate time series [J]. Pattern Recognition and Artificial Intelligence,2007, 20(3):336-342.
105 王欣.两阶段的多元时间序列异常检测算法[J].计算机应用研究,2011,28(7):2466-2469. WANGX. Two-stage multivariate time series anomaly detection algorithm [J]. Computer Application Research, 2011, 28(7): 2466-2469.
106 陈运文,吴飞,吴庐山,等.基于异常检测的时间序列研究[J].计算机技术与发展,2015(4):166-170. CHENY W, WUF, WUL S, et al. Time series research based on anomaly detection [J]. Computer Technology and Development, 2015(4):166-170.
107 陈光英,张千里,李星.基于SVM分类机的入侵检测系统[J]. 通信学报, 2002, 23(5):51-56. CHENG Y, ZHANGQ L, LIX. Intrusion detection system based on SVM classifier [J]. Journal of China Institute of Communications, 2002, 23(5):51-56.
108 KAURR, KANGS S. An enhancement in classifier support vector machine to improve plant disease detection [C]// Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education. Piscataway: IEEE, 2016: 135-140.
109 RALAIVOLAL, D’ALCHE-BUCF. Incremental support vector machine learning: a local approach [C]// Proceedings of the 2001 International Conference on Artificial Neural Networks, LNCS 2130. Berlin: Springer, 2001: 322-330.
110 孙德山.支持向量机分类与回归方法研究[D].长沙：中南大学,2004：17-18. SUND S. Research on classification and regression of support vector machines [D]. Changsha: Central South University, 2004：17-18.
111 HUM, JIZ, YANK, et al. Detecting anomalies in time series data via a meta-feature based approach [J]. IEEE Access, 2018, 6: 27760-27776.

[1]	胡天杰, 胡文军, 王士同. 分布熵惩罚的支持向量数据描述[J]. 计算机应用, 2021, 41(8): 2212-2218.
[2]	孟凡, 陈广, 王勇, 高阳, 高德群, 贾文龙. 基于多粒度时序结构表示的异常检测算法在储层含油性检测中应用[J]. 计算机应用, 2021, 41(8): 2453-2459.
[3]	姚杰, 程春玲, 韩静, 刘峥. 云工作流中基于多任务时序卷积网络的异常检测方法[J]. 计算机应用, 2021, 41(6): 1701-1708.
[4]	谢雨, 蒋瑜, 龙超奇. 基于随机子空间的扩展隔离林算法[J]. 计算机应用, 2021, 41(6): 1679-1685.
[5]	李衍志, 范勇, 高琳. 基于形态流的石油钻井水流异常检测[J]. 计算机应用, 2021, 41(6): 1842-1848.
[6]	张晨曦, 唐曙, 唐珂. 迁移学习下的火箭发动机参数异常检测策略[J]. 计算机应用, 2020, 40(9): 2774-2780.
[7]	王磊. 改进粗糙集属性约简结合K-means聚类的网络入侵检测方法[J]. 计算机应用, 2020, 40(7): 1996-2002.
[8]	仇媛, 常相茂, 仇倩, 彭程, 苏善婷. 基于长短期记忆网络和滑动窗口的流数据异常检测方法[J]. 计算机应用, 2020, 40(5): 1335-1339.
[9]	霍纬纲, 王慧芳. 基于自编码器和隐马尔可夫模型的时间序列异常检测方法[J]. 计算机应用, 2020, 40(5): 1329-1334.
[10]	夏彬, 白宇轩, 殷俊杰. 基于生成对抗网络的系统日志级异常检测算法[J]. 计算机应用, 2020, 40(10): 2960-2966.
[11]	王伟, 谢耀滨, 尹青. 针对不平衡数据的决策树改进方法[J]. 计算机应用, 2019, 39(3): 623-628.
[12]	陶涛, 周喜, 马博, 赵凡. 基于双向LSTM的Seq2Seq模型在加油站时序数据异常检测中的应用[J]. 计算机应用, 2019, 39(3): 924-929.
[13]	刘子豪, 李凌, 叶枫. 基于SparkR的水文传感器数据的异常检测方法[J]. 计算机应用, 2019, 39(2): 436-440.
[14]	丁景全, 马博, 李晓. 基于融合时空数据的车辆加油行为多视图深度异常检测框架[J]. 计算机应用, 2019, 39(11): 3370-3375.
[15]	钟雅, 郭渊博. 基于机器学习的日志解析系统设计与实现[J]. 计算机应用, 2018, 38(2): 352-356.

多维时间序列异常检测算法综述

Review of anomaly detection algorithms for multidimensional time series

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics