Package: spectralAnomaly 0.1.1
spectralAnomaly: Detect Anomalies Using the 'Spectral Residual' Algorithm
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>.
Authors:
spectralAnomaly_0.1.1.tar.gz
spectralAnomaly_0.1.1.zip(r-4.5)spectralAnomaly_0.1.1.zip(r-4.4)spectralAnomaly_0.1.1.zip(r-4.3)
spectralAnomaly_0.1.1.tgz(r-4.4-any)spectralAnomaly_0.1.1.tgz(r-4.3-any)
spectralAnomaly_0.1.1.tar.gz(r-4.5-noble)spectralAnomaly_0.1.1.tar.gz(r-4.4-noble)
spectralAnomaly_0.1.1.tgz(r-4.4-emscripten)spectralAnomaly_0.1.1.tgz(r-4.3-emscripten)
spectralAnomaly.pdf |spectralAnomaly.html✨
spectralAnomaly/json (API)
NEWS
# Install 'spectralAnomaly' in R: |
install.packages('spectralAnomaly', repos = c('https://al-obrien.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/al-obrien/spectralanomaly/issues
Last updated 2 months agofrom:16b031a122. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-win | OK | Nov 15 2024 |
R-4.5-linux | OK | Nov 15 2024 |
R-4.4-win | OK | Nov 15 2024 |
R-4.4-mac | OK | Nov 15 2024 |
R-4.3-win | OK | Nov 15 2024 |
R-4.3-mac | OK | Nov 15 2024 |
Exports:anomaly_scoreanomaly_threshsaliency_map
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Create anomaly score from input data | anomaly_score |
Apply threshold to anomaly score | anomaly_thresh |
Create saliency map | saliency_map |