Anomaly-Detection-in-R

• Anomaly is defined as something unusual and deviates from the norm or from the general data distribution.
• The goal is to analyze a time series data and find data points that do not behave normally. This is different from dealing with outliers which can be defined as anything that deviates from a given quartile range.

Published version

• The script is published in Rpubs.
• Rpubs is a free publishing site for r-markdowns and can be implemented from within R-Studio, after generating Knitr file.

https://rpubs.com/devshrestha/AnomalyDetection

AnomalyDetection R package

• Source:

https://github.com/twitter/AnomalyDetection

• AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The AnomalyDetection package can be used in wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an A/B test, or for problems in econometrics, financial engineering, political and social sciences.

Installation guide

• Install the R package using the following commands on the R console:

1. install.packages("devtools")
2. devtools::install_github("twitter/AnomalyDetection")
3. library(AnomalyDetection)

API

Built a basic model that usese in-built packages in R and used to test it on a data frame. The model produced a list of possible anomalies that might need attention in a real industry scenario.

Steps to run the script

• Download and install R-server and R-Studio.
• Clone the repo
• Set the path of the cereals file in the read.CSV
• Run the applicaton on R-Studio