Real Time Anomaly Detection And Categorisation

Fisch, Alexander T. M.; Bardwell, Lawrence; Eckley, Idris A.

doi:10.48550/arxiv.2009.06670

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…Also of interest for this application is being able to detect collective anomalies in real-time. The CAPA framework we have adopted has been shown to be able to be applied in online settings (Fisch et al, 2020), and similar ideas could be used to produce a sequential version of CAPA-CC.…”

Section: Discussionmentioning

confidence: 99%

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Tveten¹,

Eckley²,

Fearnhead³

2020

Preprint

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Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently we explore a computationally efficient approximation of the maximum likelihood solution to the resulting modelling framework, and develop a new dynamic programming algorithm for solving the resulting Binary Quadratic Programme when the precision matrix of the time series at any given time-point is banded. Through a comprehensive simulation study, we show that the resulting methods perform favourably compared to competing methods both in the anomaly and change detection settings, even when the sparsity structure of the precision matrix estimate is misspecified. We also demonstrate its ability to correctly detect faulty time-periods of a pump within the motivating application.

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Section: Discussionmentioning

confidence: 99%

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Tveten¹,

Eckley²,

Fearnhead³

2020

Preprint

Self Cite

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“…The method is suitable when collective anomalies are characterized by either a change in mean, variance, or both, ant it is capable of distinguishing collective anomalies from point anomalies. This and several other methods have been implemented in an R package by Fisch et al, 23 where we also find the multi-variate collective and point anomaly (MVCAPA) method of Reference 24, the proportion adaptive segment selection (PASS) method of Reference 25, the Bayesian abnormal region detector (BARD) of Reference 26, and also sequential versions of CAPA and MVCAPA by Fisch et al 27 Fisch 15 provides the state of the art on statistical anomaly detection, together with a guide for computational implementation.…”

Section: Introductionmentioning

confidence: 88%

Detecting systematic anomalies affecting systems when inputs are stationary time series

Sun

Yang

Zitikis

2022

Appl Stoch Models Bus & Ind

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We develop an anomaly detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally nonstationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly free inputs following an ARMA time series model under various contamination scenarios.

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“…As mentioned in Section 1, here we show how our method can be applied to the online framework. We refer the reader to Fisch et al (2020) and Yu et al (2021) for the recent works on online detection algorithm for change-points or anomalies. In the online setting, we make sequential decisions about the occurrence of an anomaly whenever each new observations is obtained.…”

Section: M3 M4mentioning

confidence: 99%

Collective Anomaly Detection in High-Dimensional Var Models

Maeng¹,

Eckley²,

Fearnhead³

2024

STAT SINICA

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There is increasing interest in detecting collective anomalies: potentially short periods of time where the features of data change, before reverting back to normal behaviour. We propose a new method for detecting a collective anomaly in VAR models.Our focus is on situations where the change in the VAR coefficient matrix at an anomaly is sparse, i.e. a small number of entries of the VAR coefficient matrix change. To tackle this problem, we propose a test statistic for a local segment that is built on the lasso estimator of the change in model parameters. This enables us to detect a sparse change more efficiently, and our lasso-based approach becomes especially advantageous when the anomalous interval is short. We show that the new procedure controls Type 1 error and has asymptotic power tending to one. The practicality of our approach is demonstrated through simulations and two data examples, involving New York taxi trip data and EEG data.

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Real Time Anomaly Detection And Categorisation

Cited by 6 publications

References 28 publications

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Detecting systematic anomalies affecting systems when inputs are stationary time series

Collective Anomaly Detection in High-Dimensional Var Models

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