Detecting Anomalies in Data

Implements Collective And Point Anomaly (CAPA) , Multi-Variate Collective And Point Anomaly (MVCAPA) , and Proportion Adaptive Segment Selection (PASS) methods for the detection of anomalies in time series data.

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.


Reference manual

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2.0.1 by Daniel Grose, 5 days ago

Browse source code at

Authors: Alex Fisch [aut] , Daniel Grose [aut, cre] , Lawrence Bardwell [ctb] , Idris Eckley [ths] , Paul Fearnhead [ths]

Documentation:   PDF Manual  

GPL license

Imports dplyr, rlang, methods, assertive, Rdpack, ggplot2, reshape2, Rcpp, robust

Suggests magrittr

Linking to Rcpp, BH

See at CRAN