Tidy Anomaly Detection
Detect anomalies using the tidyverse
Methods that power anomalize()
anomalize: Tidy anomaly detection
Clean anomalies from anomalized data
Methods that power time_decompose()
Visualize the anomalies in one or multiple time series
Visualize the time series decomposition with anomalies shown
Automatically create tibbletime objects from tibbles
Apply a function to a time series by period
Decompose a time series in preparation for anomaly detection
Generate a time series frequency from a periodicity
Recompose bands separating anomalies from "normal" observations
Get and modify time scale template
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.
Useful links