Online Time Series Anomaly Detectors

Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter et al. (2012) , SD-EWMA and TSSD-EWMA see H. Raza et al. (2015) , 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.


Reference manual

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0.2.0 by Alaiñe Iturria, 2 years ago

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Authors: Alaiñe Iturria [aut, cre] , Jacinto Carrasco [aut] , Francisco Herrera [aut] , Santiago Charramendieta [aut] , Karmele Intxausti [aut]

Documentation:   PDF Manual  

Task views: Time Series Analysis

AGPL (>= 3) license

Imports stats, ggplot2, plotly, sigmoid, reticulate

Suggests testthat, stream, knitr, rmarkdown

System requirements: Python (>= 3.0.1); bencode-python3 (1.0.2)

Imported by composits.

See at CRAN