CRAN/E | oddstream

oddstream

Outlier Detection in Data Streams

Installation

About

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. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) doi:10.1080/10618600.2019.1617160.

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Key Metrics

Version 0.5.0
R ≥ 3.4.0
Published 2019-12-16 1761 days ago
Needs compilation? no
License GPL-3
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Maintainer

Maintainer

Priyanga Dilini Talagala

Authors

Priyanga Dilini Talagala

aut / cre

Rob J. Hyndman

ths

Kate Smith-Miles

ths

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.4.0

Imports

pcaPP
stats
ggplot2
ks
MASS
RcppRoll
mgcv
moments
RColorBrewer
mvtsplot
tibble
reshape
dplyr
graphics
tidyr
kernlab
magrittr

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