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) <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 |
| In views: |
AnomalyDetection |
| CRAN checks: |
oddnet results |
Documentation:
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