oddnet: Anomaly Detection in Temporal Networks

Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Version: 0.1.0
Imports: dplyr, fable, fabletools, igraph, lookout, pcaPP, rlang, tibble, tidyr, tsibble, utils
Suggests: DDoutlier, feasts, knitr, rmarkdown, rTensor, urca
Published: 2022-12-22
Author: Sevvandi Kandanaarachchi ORCID iD [aut, cre], Rob Hyndman ORCID iD [aut]
Maintainer: Sevvandi Kandanaarachchi <sevvandik at gmail.com>
License: GPL (≥ 3)
URL: https://sevvandi.github.io/oddnet/
NeedsCompilation: no
Materials: README
CRAN checks: oddnet results


Reference manual: oddnet.pdf
Vignettes: oddnet


Package source: oddnet_0.1.0.tar.gz
Windows binaries: r-devel: oddnet_0.1.0.zip, r-release: oddnet_0.1.0.zip, r-oldrel: oddnet_0.1.0.zip
macOS binaries: r-release (arm64): oddnet_0.1.0.tgz, r-oldrel (arm64): oddnet_0.1.0.tgz, r-release (x86_64): oddnet_0.1.0.tgz, r-oldrel (x86_64): oddnet_0.1.0.tgz


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