anomaly v2.0.1


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Detecting Anomalies in Data

Implements Collective And Point Anomaly (CAPA) <arXiv:1806.01947>, Multi-Variate Collective And Point Anomaly (MVCAPA) <arXiv:1909.01691>, and Proportion Adaptive Segment Selection (PASS) <doi:10.1093/biomet/ass059> 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.

Functions in anomaly

Name Description
capa A technique for detecting anomalous segments and points based on CAPA.
simulate A function for generating simulated multivariate data
Lightcurves Kepler Lightcurve data.
machinetemp Machine temperature data.
summary Summary of collective and point anomalies. Detection of multivariate anomalous segments and points using MVCAPA.
anomaly_series A technique for detecting anomalous segments based on CAPA.
ac_corrected Transforms the data X to account for autocorrelation.
period_average A function to search the Kepler data for periodically recurring dips in luminosity.
pass Detection of multivariate anomalous segments using PASS.
plot Visualisation of data, collective and point anomalies.
collective_anomalies Collective anomaly location, lags, and mean/variance changes.
capa.uv Detection of univariate anomalous segments and points using CAPA.
robustscale robustscale
point_anomalies Point anomaly location and strength.
show Displays S4 objects produced by capa methods.
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Type Package
Date 2019-09-04
License GPL
LinkingTo Rcpp,BH
NeedsCompilation yes
RoxygenNote 6.1.1
RdMacros Rdpack
Packaged 2019-09-11 09:18:15 UTC; grosedj1
Repository CRAN
Date/Publication 2019-09-11 11:00:08 UTC

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