anomalize

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)

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:

• time_decompose(): Separates the time series into seasonal, trend, and remainder components
• anomalize(): Applies anomaly detection methods to the remainder component.
• time_recompose(): Calculates limits that separate the “normal” data from the anomalies!

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.

tidyverse_cran_downloads %>%
    ggplot(aes(date, count)) +
    geom_point(color = "#2c3e50", alpha = 0.25) +
    facet_wrap(~ package, scale = "free_y", ncol = 3) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
    labs(title = "Tidyverse Package Daily Download Counts",
         subtitle = "Data from CRAN by way of cranlogs package")