Detecting Anomalies in Data
Detecting Anomalies in Data
Detection of multivariate anomalous segments using BARD.
A technique for detecting anomalous segments and points based on CAPA.
Collective anomaly location, lags, and mean/variance changes.
Machine temperature data.
Detection of multivariate anomalous segments using PASS.
Visualisation of data, collective and point anomalies.
Point anomaly location and strength.
Post processing of BARD results.
Displays S4 objects produced by capa methods.
Simulated data.
Summary of collective and point anomalies.
Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.