The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 |
Reference manual: | anomalize.pdf |
Vignettes: |
Anomalize Methods Anomalize Quick Start Guide Forecasting with Cleaned Anomalies |
Package source: | anomalize_0.3.0.tar.gz |
Windows binaries: | r-devel: anomalize_0.3.0.zip, r-release: anomalize_0.3.0.zip, r-oldrel: anomalize_0.3.0.zip |
macOS binaries: | r-release (arm64): anomalize_0.3.0.tgz, r-oldrel (arm64): anomalize_0.3.0.tgz, r-release (x86_64): anomalize_0.3.0.tgz, r-oldrel (x86_64): anomalize_0.3.0.tgz |
Old sources: | anomalize archive |
Reverse suggests: | pathviewr, whippr |
Please use the canonical form https://CRAN.R-project.org/package=anomalize to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.