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cff-version: 1.2.0
message: 'To cite package "anomalize" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'anomalize: Tidy Anomaly Detection'
version: 0.3.0.9000
doi: 10.32614/CRAN.package.anomalize
abstract: The 'anomalize' package enables a "tidy" workflow for detecting anomalies
  in data. The main functions are time_decompose(), anomalize(), and time_recompose().
  When combined, it's quite simple to decompose time series, detect anomalies, and
  create bands separating the "normal" data from the anomalous data at scale (i.e.
  for multiple time series). Time series decomposition is used to remove trend and
  seasonal components via the time_decompose() function and methods include seasonal
  decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise
  medians ("twitter"). The anomalize() function implements two methods for anomaly
  detection of residuals including using an inner quartile range ("iqr") and generalized
  extreme studentized deviation ("gesd"). These methods are based on those used in
  the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the
  associated functions for specific references for these methods.
authors:
- family-names: Dancho
  given-names: Matt
  email: mdancho@business-science.io
- family-names: Vaughan
  given-names: Davis
  email: dvaughan@business-science.io
repository: https://business-science.r-universe.dev
repository-code: https://github.com/business-science/anomalize
commit: f5d37063c83bb0b4b4256aed81dead489414b89c
url: https://business-science.github.io/anomalize/
contact:
- family-names: Dancho
  given-names: Matt
  email: mdancho@business-science.io