@article{AHMAD2017134,
title = "Unsupervised real-time anomaly detection for streaming data",
journal = "Neurocomputing",
volume = "262",
pages = "134 - 147",
year = "2017",
note = "Online Real-Time Learning Strategies for Data Streams",
issn = "0925-2312",
doi = "10.1016/j.neucom.2017.04.070",
url = "https://www.sciencedirect.com/science/article/pii/S0925231217309864/",
author = "Subutai Ahmad and Alexander Lavin and Scott Purdy and Zuha Agha",
}

@article{bardwell2017,
author = "Bardwell, Lawrence and Fearnhead, Paul",
%eprint={1412.5565},
%archivePrefix={arXiv},
%doi = "10.1214/16-BA998",
fjournal = "Bayesian Analysis",
journal = "Bayesian Anal.",
month = "03",
number = "1",
pages = "193--218",
publisher = "International Society for Bayesian Analysis",
title = "Bayesian Detection of Abnormal Segments in Multiple Time Series",
%comment = {url = {https://doi.org/10.1214/16-BA998}},
volume = "12",
year = "2017"
}




@article{2019MVCAPA,
author = {Alexander T. M. Fisch and Idris A. Eckley and Paul Fearnhead},
title = {Subset Multivariate Collective and Point Anomaly Detection},
journal = {Journal of Computational and Graphical Statistics},
volume = {31},
number = {2},
pages = {574-585},
year  = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.2021.1987257}
}

@article{2019MVCAPA_b,
author = {Alexander T. M. Fisch and Idris A. Eckley and Paul Fearnhead},
title = {Subset Multivariate Collective and Point Anomaly Detection},
journal = {Journal of Computational and Graphical Statistics},
volume = {31},
number = {2},
pages = {574-585},
year  = {2022b},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.2021.1987257}
}

@article{2018arXiv180601947F,
author = {Fisch, Alexander T. M. and Eckley, Idris A. and Fearnhead, Paul},
title = {A linear time method for the detection of collective and point anomalies},
journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal},
volume = {15},
number = {4},
pages = {494-508},
keywords = {dynamic programming, epidemic changepoints, exoplanets, Numenta Anomaly Benchmark, outliers, robust statistics},
doi = {https://doi.org/10.1002/sam.11586},
abstract = {Abstract The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Although there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments, or collective anomalies, particularly in those settings where point anomalies might also occur. In this article, we introduce collective and point anomalies (CAPA), a computationally efficient approach that is suitable when collective anomalies are characterized by either a change in mean, variance, or both, and distinguishes them from point anomalies. Empirical results show that CAPA has close to linear computational cost as well as being more accurate at detecting and locating collective anomalies than other approaches. We demonstrate the utility of CAPA through its ability to detect exoplanets from light curve data from the Kepler telescope and its capacity to detect machine faults from temperature data.},
year = {2022}
}

@article{2018arXiv180601947F_a,
author = {Fisch, Alexander T. M. and Eckley, Idris A. and Fearnhead, Paul},
title = {A linear time method for the detection of collective and point anomalies},
journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal},
volume = {15},
number = {4},
pages = {494-508},
keywords = {dynamic programming, epidemic changepoints, exoplanets, Numenta Anomaly Benchmark, outliers, robust statistics},
doi = {https://doi.org/10.1002/sam.11586},
abstract = {Abstract The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Although there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments, or collective anomalies, particularly in those settings where point anomalies might also occur. In this article, we introduce collective and point anomalies (CAPA), a computationally efficient approach that is suitable when collective anomalies are characterized by either a change in mean, variance, or both, and distinguishes them from point anomalies. Empirical results show that CAPA has close to linear computational cost as well as being more accurate at detecting and locating collective anomalies than other approaches. We demonstrate the utility of CAPA through its ability to detect exoplanets from light curve data from the Kepler telescope and its capacity to detect machine faults from temperature data.},
year = {2022a}
}

@article{10.1093/biomet/ass059,
    author = {Jeng, X. Jessie and Cai, T. Tony and Li, Hongzhe},
    title = "{Simultaneous discovery of rare and common segment variants}",
    journal = {Biometrika},
    volume = {100},
    number = {1},
    pages = {157-172},
    year = {2012},
    month = {12},
    abstract = "{Copy number variant is an important type of genetic structural variation appearing in germline DNA, ranging from common to rare in a population. Both rare and common copy number variants have been reported to be associated with complex diseases, so it is important to identify both simultaneously based on a large set of population samples. We develop a proportion adaptive segment selection procedure that automatically adjusts to the unknown proportions of the carriers of the segment variants. We characterize the detection boundary that separates the region where a segment variant is detectable by some method from the region where it cannot be detected. Although the detection boundaries are very different for the rare and common segment variants, it is shown that the proposed procedure can reliably identify both whenever they are detectable. Compared with methods for single-sample analysis, this procedure gains power by pooling information from multiple samples. The method is applied to analyse neuroblastoma samples and identifies a large number of copy number variants that are missed by single-sample methods.}",
    issn = {0006-3444},
    doi = {10.1093/biomet/ass059},
    url = {https://academic.oup.com/biomet/article/100/1/157/193108}
}




@article{ecp-package,
    title = {{ecp}: An {R} Package for Nonparametric Multiple Change Point Analysis of Multivariate Data},
    author = {Nicholas A. James and David S. Matteson},
    journal = {Journal of Statistical Software},
    year = {2014},
    volume = {62},
    number = {7},
    pages = {1--25},
    url = {http://www.jstatsoft.org/v62/i07/},
  }


@article{Schruben:1983:OTI:2771114.2771123,
 author = {Schruben, L. and Singh, H. and Tierney, L.},
 title = {Optimal Tests for Initialization Bias in Simulation Output},
 journal = {Oper. Res.},
 issue_date = {December 1983},
 volume = {31},
 number = {6},
 month = dec,
 year = {1983},
 issn = {0030-364X},
 pages = {1167--1178},
 numpages = {12},
 url = {http://dx.doi.org/10.1287/opre.31.6.1167},
 doi = {10.1287/opre.31.6.1167},
 acmid = {2771123},
 publisher = {INFORMS},
 address = {Institute for Operations Research and the Management Sciences (INFORMS), Linthicum, Maryland, USA},
 keywords = {767 statistical analysis},
}

@misc{alex2020real,
    title={Real Time Anomaly Detection And Categorisation},
    author={Alexander T. M. Fisch and Lawrence Bardwell and Idris A. Eckley},
    year={2020},
    eprint={2009.06670},
    archivePrefix={arXiv},
    primaryClass={stat.ME}
}

@article{donoho2004,
author = "Donoho, David and Jin, Jiashun",
doi = "10.1214/009053604000000265",
fjournal = "The Annals of Statistics",
journal = "The Annals of Statistics",
month = "06",
number = "3",
pages = "962--994",
publisher = "The Institute of Mathematical Statistics",
title = "Higher Criticism for Detecting Sparse Heterogeneous Mixtures",
comment = {url = {https://doi.org/10.1214/009053604000000265}},
volume = "32",
year = "2004"
}
