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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi = 200,
message = F,
warning = F
)
devtools::load_all()
library(tidyverse)
```
<!-- # anomalize -->
# anomalize <img src="man/figures/anomalize-logo.png" width="147" height="170" align="right" />
[](https://travis-ci.org/business-science/anomalize)
[](https://codecov.io/github/business-science/anomalize?branch=master)
[](https://cran.r-project.org/package=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)
<a href="https://www.youtube.com/watch?v=Gk_HwjhlQJs" target="_blank"><img src="http://img.youtube.com/vi/Gk_HwjhlQJs/0.jpg"
alt="Anomalize" width="100%" height="350"/></a>
Check out our entire [Software Intro Series](https://www.youtube.com/watch?v=Gk_HwjhlQJs&list=PLo32uKohmrXsYNhpdwr15W143rX6uMAze) on YouTube!
## Installation
You can install the development version with `devtools` or the most recent CRAN version with `install.packages()`:
``` r
# 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.
```{r, eval = F}
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()`.
```{r tidyverse_anoms_1, fig.height=8}
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](https://business-science.github.io/anomalize/articles/anomalize_quick_start_guide.html).
## 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](https://business-science.github.io/anomalize/articles/forecasting_with_cleaned_anomalies.html).
```{r}
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")
```
## But Wait, There's More!
There are a several extra capabilities:
- `plot_anomaly_decomposition()` for visualizing the inner workings of how algorithm detects anomalies in the "remainder".
```{r, fig.height=7}
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](https://business-science.github.io/anomalize/articles/anomalize_methods.html).
## References
Several other packages were instrumental in developing anomaly detection methods used in `anomalize`:
- Twitter's `AnomalyDetection`, which implements decomposition using median spans and the Generalized Extreme Studentized Deviation (GESD) test for anomalies.
- `forecast::tsoutliers()` function, which implements the IQR method.
# Interested in Learning Anomaly Detection?
Business Science offers two 1-hour courses on Anomaly Detection:
- [Learning Lab 18](https://university.business-science.io/p/learning-labs-pro) - Time Series Anomaly Detection with `anomalize`
- [Learning Lab 17](https://university.business-science.io/p/learning-labs-pro) - Anomaly Detection with `H2O` Machine Learning