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Apply the spectral residual algorithm to data, such as a time series, to detect anomalies. Anomaly scores can be used to determine outliers based upon a threshold or fed into more sophisticated prediction models. Methods are based upon "Time-Series Anomaly Detection Service at Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019) <doi:10.48550/arXiv.1906.03821>.
Version: | 0.1.1 |
Imports: | stats, utils |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2024-09-15 |
DOI: | 10.32614/CRAN.package.spectralAnomaly |
Author: | Allen OBrien [aut, cre, cph] |
Maintainer: | Allen OBrien <allen.g.obrien at gmail.com> |
BugReports: | https://github.com/al-obrien/spectralAnomaly/issues |
License: | MIT + file LICENSE |
URL: | https://al-obrien.github.io/spectralAnomaly/, https://github.com/al-obrien/spectralAnomaly |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | spectralAnomaly results |
Reference manual: | spectralAnomaly.pdf |
Package source: | spectralAnomaly_0.1.1.tar.gz |
Windows binaries: | r-devel: spectralAnomaly_0.1.1.zip, r-release: spectralAnomaly_0.1.1.zip, r-oldrel: spectralAnomaly_0.1.1.zip |
macOS binaries: | r-release (arm64): spectralAnomaly_0.1.1.tgz, r-oldrel (arm64): spectralAnomaly_0.1.1.tgz, r-release (x86_64): spectralAnomaly_0.1.1.tgz, r-oldrel (x86_64): spectralAnomaly_0.1.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.