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#+TITLE: An Introduction into Anomaly Detection #+AUTHOR: Dominik Dahlem #+EMAIL: dominik.dahlem@boxever.com #+DATE: 2015-10-27 Tue #+LANGUAGE: en

• Introduction

This project gives a high-level overview of anomaly detection in timeseries data and provides a basic implementation of the cumulative sum (CUSUM) algorithm in R. CUSUM relies on stationarity assumptions of the timeseries, which constraints its use to real-world problems somewhat. However, timeseries data can often be annotated, such that piece-wise stationarity holds. For example, we could divide the timeseries into hourly buckets where stationarity assumptions hold with respect to these buckets.

This introduction was presented to an R meetup in Dublin. The [[file:anomaly.org][slides]] are available in the org-mode format and can be exported to a latex-beamer presentation. All required packages are part of the latest TexLive distribution. I tested the PDF creation with luatex and biber for the bibliography.

• Installation

** Dependencies The R implementation relies on a /k/-nearest neighbour algorithm from the FNN library. The optimx package is used to tune the anomaly detection algorithm on a particular data set. Otherwise, no further packages are required to run the scripts.

• Open an R session
• Type ~install.packages(c("FNN", "optimx"), dependencies=T)~ to install the required packages

** CUSUM package

You can tangle the workflow in [[file:anomaly.org]] first in order to create the R package structure. Before proceeding with the workflow, install the ~cusum~ R package:

• ~cd src/package~
• ~R CMD INSTALL cusum~