anomaly: : Detection of Anomalous Structure in Time Series Data

被引:0
作者
Fisch, Alex [1 ]
Grose, Daniel [1 ]
Eckley, Idris A. [1 ]
Fearnhead, Paul [1 ]
Bardwell, Lawrence [1 ]
机构
[1] Univ Lancaster, Lancaster, England
基金
英国工程与自然科学研究理事会;
关键词
anomaly detection; point anomaly; collective anomaly; BARD; CAPA; CHANGE-POINT; R PACKAGE; NUMBER;
D O I
10.18637/jss.v110.i01
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed collective and point anomaly family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.
引用
收藏
页码:1 / 24
页数:24
相关论文
共 34 条
[1]   Unsupervised real-time anomaly detection for streaming data [J].
Ahmad, Subutai ;
Lavin, Alexander ;
Purdy, Scott ;
Agha, Zuha .
NEUROCOMPUTING, 2017, 262 :134-147
[2]   A survey of anomaly detection techniques in financial domain [J].
Ahmed, Mohiuddin ;
Mahmood, Abdun Naser ;
Islam, Md. Rafiqul .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2016, 55 :278-288
[3]  
Arundo Analytics Inc, 2020, adtk: A Package for Unsepervised Time Series Anomaly Detection
[4]   Bayesian Detection of Abnormal Segments in Multiple Time Series [J].
Bardwell, Lawrence ;
Fearnhead, Paul .
BAYESIAN ANALYSIS, 2017, 12 (01) :193-218
[5]  
Bleakley K, 2011, Arxiv, DOI arXiv:1106.4199
[6]   Optimal detection of heterogeneous and heteroscedastic mixtures [J].
Cai, T. Tony ;
Jeng, X. Jessie ;
Jin, Jiashun .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2011, 73 :629-662
[7]  
Dancho Matt, 2023, CRAN
[8]   Higher criticism for detecting sparse heterogeneous mixtures [J].
Donoho, D ;
Jin, JS .
ANNALS OF STATISTICS, 2004, 32 (03) :962-994
[9]   A linear time method for the detection of collective and point anomalies [J].
Fisch, Alexander T. M. ;
Eckley, Idris A. ;
Fearnhead, Paul .
STATISTICAL ANALYSIS AND DATA MINING, 2022, 15 (04) :494-508
[10]   Subset Multivariate Collective and Point Anomaly Detection [J].
Fisch, Alexander T. M. ;
Eckley, Idris A. ;
Fearnhead, Paul .
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2022, 31 (02) :574-585