pridiltal/oddstream: Outlier Detection in Data Streams
Version 0.1.0

We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically.

Getting started

Package details

AuthorPriyanga Dilini Talagala [aut, cre] Rob J. Hyndman [aut, ths] Kate Smith-Miles [aut, ths]
MaintainerPriyanga Dilini Talagala <[email protected]>
LicenseGPL-3
Version0.1.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("pridiltal/oddstream")
pridiltal/oddstream documentation built on April 3, 2018, 1:08 p.m.