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otsad

Online Time Series Anomaly Detectors

Installation

About

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) , KNN-LDCD see V. Ishimtsev et al. (2017) and CAD-OSE see M. Smirnov (2018) . 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.

Citation otsad citation info
github.com/alaineiturria/otsad
System requirements Python (>= 3.0.1); bencode-python3 (1.0.2)
Bug report File report

Key Metrics

Version 0.2.0
R ≥ 3.4.0
Published 2019-09-06 1700 days ago
Needs compilation? no
License AGPL (≥ 3)
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Maintainer

Maintainer

Alaiñe Iturria

aiturria@ikerlan.es

Authors

Alaiñe Iturria

aut / cre

Jacinto Carrasco

aut

Francisco Herrera

aut

Santiago Charramendieta

aut

Karmele Intxausti

aut

Material

README
NEWS
Reference manual
Package source

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The otsad Package: Online Time-Series Anomaly Detectors

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

otsad archive

Depends

R ≥ 3.4.0

Imports

stats
ggplot2
plotly
sigmoid
reticulate

Suggests

testthat
stream
knitr
rmarkdown

Reverse Imports

composits