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) <doi:10.48550/arXiv.2210.07407>.
Version: 0.1.1 Imports: dplyr, fable, fabletools, igraph, lookout, pcaPP, rlang, tibble, tidyr, tsibble, utils Suggests: DDoutlier, feasts, knitr, rmarkdown, rTensor, urca Published: 2024-02-11 DOI: 10.32614/CRAN.package.oddnet Author: Sevvandi Kandanaarachchi [aut, cre], Rob Hyndman [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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=oddnet to link to this page.
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