Package: anomalize 0.3.0.9000

Matt Dancho

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.

Authors:Matt Dancho [aut, cre], Davis Vaughan [aut]

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NEWS

# Installanomalize in R:
install.packages('anomalize',repos = c('https://business-science.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/business-science/anomalize/issues

Datasets:

On CRAN:

anomalyanomaly-detectiondecompositiondetect-anomaliesiqrtime-series

17 exports 337 stars 6.50 score 134 dependencies 2 mentions 1.8k downloads

Last updated 6 months agofrom:f5d37063c8

Exports:anomalizeclean_anomaliesdecompose_stldecompose_twittergesdget_time_scale_templateiqrplot_anomaliesplot_anomaly_decompositionprep_tbl_timeset_time_scale_templatetime_applytime_decomposetime_frequencytime_recomposetime_scale_templatetime_trend

Dependencies:anytimeaskpassassertthatbackportsbase64encBHbitbit64broombslibcachemclassclicliprclockcodetoolscolorspacecpp11crayoncrosstalkcurldata.tablediagramdigestdplyrellipsisevaluatefansifarverfastmapfontawesomeforcatsforecastfracdifffsfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathighrhmshtmltoolshtmlwidgetshttripredisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlmtestlubridatemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetnumDerivopensslpadrparallellypillarpkgconfigplotlyprettyunitsprodlimprogressprogressrpromisespurrrquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppRollreadrrecipesrlangrmarkdownrpartrsamplesassscalesshapesliderSQUAREMstringistringrsurvivalsweepsystibbletibbletimetidyrtidyselecttimechangetimeDatetimetktinytextseriestsfeaturesTTRtzdburcautf8vctrsviridisLitevroomwarpwithrxfunxtsyamlzoo

Anomalize Methods

Rendered fromanomalize_methods.Rmdusingknitr::rmarkdownon Jun 28 2024.

Last update: 2023-12-06
Started: 2018-03-22

Anomalize Quick Start Guide

Rendered fromanomalize_quick_start_guide.Rmdusingknitr::rmarkdownon Jun 28 2024.

Last update: 2023-12-06
Started: 2018-03-22

Reduce Forecast Error with Cleaned Anomalies

Rendered fromforecasting_with_cleaned_anomalies.Rmdusingknitr::rmarkdownon Jun 28 2024.

Last update: 2023-12-06
Started: 2019-09-20