otsad: Online Time Series Anomaly Detectors
Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al.
(2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
<arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and
CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three
algorithms belong to prediction-based techniques and the last three belong to
window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms
designed to work in stationary environments, while the other four
are algorithms designed to work in non-stationary environments.
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
stats, ggplot2, plotly, sigmoid, reticulate |
| Suggests: |
testthat, stream, knitr, rmarkdown |
| Published: |
2019-09-06 |
| Author: |
Alaiñe Iturria [aut, cre],
Jacinto Carrasco [aut],
Francisco Herrera [aut],
Santiago Charramendieta [aut],
Karmele Intxausti [aut] |
| Maintainer: |
Alaiñe Iturria <aiturria at ikerlan.es> |
| BugReports: |
https://github.com/alaineiturria/otsad/issues |
| License: |
AGPL (≥ 3) |
| URL: |
https://github.com/alaineiturria/otsad |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.0.1); bencode-python3 (1.0.2) |
| Citation: |
otsad citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
otsad results |
Documentation:
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