anomalize

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Tidy anomaly detection

anomalize 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.

Anomalize In 2 Minutes (YouTube)

Anomalize

Check out our entire Software Intro Series on YouTube!

Installation

You can install the development version with devtools or the most recent CRAN version with install.packages():

# devtools::install_github("business-science/anomalize")
install.packages("anomalize")

How It Works

anomalize has three main functions:

Getting Started

Load the tidyverse and anomalize packages.

library(tidyverse)
library(anomalize)

Next, let’s get some data. anomalize ships with a data set called tidyverse_cran_downloads that contains the daily CRAN download counts for 15 “tidy” packages from 2017-01-01 to 2018-03-01.

Suppose we want to determine which daily download “counts” are anomalous. It’s as easy as using the three main functions (time_decompose(), anomalize(), and time_recompose()) along with a visualization function, plot_anomalies().

tidyverse_cran_downloads %>%
    # Data Manipulation / Anomaly Detection
    time_decompose(count, method = "stl") %>%
    anomalize(remainder, method = "iqr") %>%
    time_recompose() %>%
    # Anomaly Visualization
    plot_anomalies(time_recomposed = TRUE, ncol = 3, alpha_dots = 0.25) +
    labs(title = "Tidyverse Anomalies", subtitle = "STL + IQR Methods") 

Check out the anomalize Quick Start Guide.

Reducing Forecast Error by 32%

Yes! Anomalize has a new function, clean_anomalies(), that can be used to repair time series prior to forecasting. We have a brand new vignette - Reduce Forecast Error (by 32%) with Cleaned Anomalies.

tidyverse_cran_downloads %>%
    filter(package == "lubridate") %>%
    ungroup() %>%
    time_decompose(count) %>%
    anomalize(remainder) %>%
  
    # New function that cleans & repairs anomalies!
    clean_anomalies() %>%
  
    select(date, anomaly, observed, observed_cleaned) %>%
    filter(anomaly == "Yes")
#> # A time tibble: 19 x 4
#> # Index: date
#>    date       anomaly  observed observed_cleaned
#>    <date>     <chr>       <dbl>            <dbl>
#>  1 2017-01-12 Yes     -1.14e-13            3522.
#>  2 2017-04-19 Yes      8.55e+ 3            5202.
#>  3 2017-09-01 Yes      3.98e-13            4137.
#>  4 2017-09-07 Yes      9.49e+ 3            4871.
#>  5 2017-10-30 Yes      1.20e+ 4            6413.
#>  6 2017-11-13 Yes      1.03e+ 4            6641.
#>  7 2017-11-14 Yes      1.15e+ 4            7250.
#>  8 2017-12-04 Yes      1.03e+ 4            6519.
#>  9 2017-12-05 Yes      1.06e+ 4            7099.
#> 10 2017-12-27 Yes      3.69e+ 3            7073.
#> 11 2018-01-01 Yes      1.87e+ 3            6418.
#> 12 2018-01-05 Yes     -5.68e-14            6293.
#> 13 2018-01-13 Yes      7.64e+ 3            4141.
#> 14 2018-02-07 Yes      1.19e+ 4            8539.
#> 15 2018-02-08 Yes      1.17e+ 4            8237.
#> 16 2018-02-09 Yes     -5.68e-14            7780.
#> 17 2018-02-10 Yes      0.                  5478.
#> 18 2018-02-23 Yes     -5.68e-14            8519.
#> 19 2018-02-24 Yes      0.                  6218.

But Wait, There’s More!

There are a several extra capabilities:

tidyverse_cran_downloads %>%
    filter(package == "lubridate") %>%
    ungroup() %>%
    time_decompose(count) %>%
    anomalize(remainder) %>%
    plot_anomaly_decomposition() +
    labs(title = "Decomposition of Anomalized Lubridate Downloads")

For more information on the anomalize methods and the inner workings, please see “Anomalize Methods” Vignette.

References

Several other packages were instrumental in developing anomaly detection methods used in anomalize:

Interested in Learning Anomaly Detection?

Business Science offers two 1-hour courses on Anomaly Detection: