Description
Detecting Anomalies in Data.
Description
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) <arXiv:1412.5565>. These methods are for the detection of anomalies in time series data.
README.md
anomaly
Fast anomaly detection in R
In Brief
This R package implements CAPA (Collective And Point Anomalies) introduced by Fisch, Eckley and Fearnhead (2018). The package is available on CRAN and contains lightcurve data from the Kepler telescope to illustrate the algorithm.
About CAPA
CAPA detects and distinguishes between collective and point anomalies. The algorithm's runtime scales linearly at best and quadratically at worst in the number of datapoints. It is coded in C and can process 10000 datapoints almost instantly.