anomalize: Tidy Anomaly Detection

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: 0.1.1
Depends: R (≥ 3.0.0)
Imports: dplyr, glue, timetk, sweep, tibbletime, purrr, rlang, tibble, tidyr, ggplot2
Suggests: tidyverse, tidyquant, testthat, covr, knitr, rmarkdown, devtools, roxygen2
Published: 2018-04-17
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

Downloads:

Reference manual: anomalize.pdf
Vignettes: Anomalize Methods
Anomalize Quick Start Guide
Package source: anomalize_0.1.1.tar.gz
Windows binaries: r-prerel: anomalize_0.1.1.zip, r-release: anomalize_0.1.1.zip, r-oldrel: anomalize_0.1.0.zip
OS X binaries: r-prerel: anomalize_0.1.1.tgz, r-release: anomalize_0.1.1.tgz
Old sources: anomalize archive

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