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.
Version: 0.3.0 Depends: R (≥ 3.0.0) Imports: dplyr, glue, timetk, sweep, tibbletime (≥ 0.1.5), purrr, rlang, tibble, tidyr (≥ 1.0.0), ggplot2 Suggests: tidyverse, tidyquant, stringr, testthat (≥ 2.1.0), covr, knitr, rmarkdown, devtools, roxygen2 Published: 2023-10-31 DOI: 10.32614/CRAN.package.anomalize Author: Matt Dancho [aut, cre], Davis Vaughan [aut] Maintainer: Matt Dancho <mdancho at business-science.io> BugReports: https://github.com/business-science/anomalize/issues License: GPL (≥ 3) URL: https://github.com/business-science/anomalize NeedsCompilation: no Materials: README, NEWS CRAN checks: anomalize results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=anomalize to link to this page.
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