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❗ This is a read-only mirror of the CRAN R package repository. otsad — Online Time Series Anomaly Detectors. Homepage: https://github.com/alaineiturria/otsad Report bugs for this package: https://github.com/alaineiturria/otsad/issues

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otsad

Online Time Series Anomaly Detectors

This package provides anomaly detectors in the context of online time series and their evaluation with the Numenta score.

Installation

Dependencies

CAD-OSE algorithm is implemented in Python. It uses bencode library in the hashing step. This dependency can be installed with the Python package manager pip.

$ sudo pip install bencode-python3

otsad package

You can install the released version of otsad from CRAN with:

# Get the released version from CRAN
install.packages("otsad")

# Get the latest development version from GitHub
devtools::install_github("alaineiturria/otsad")

Most useful functions

Detectors

  • PEWMA
    • Offline: CpPewma
    • Online: IpPewma
  • SD-EWMA
    • Offline: CpSdEwma
    • Online: IpSdEwma
  • TSSD-EWMA
    • Offline: CpTsSdEwma
    • Online: IpTsSdEwma
  • KNN-ICAD
    • Offline: CpKnnCad(ncm.type = "ICAD")
    • Online: IpKnnCad(ncm.type = "ICAD")
  • KNN-LDCD
    • Offline CpKnnCad(ncm.type = "LDCD")
    • Online: IpKnnCad(ncm.type = "LDCD")
  • CAD-OSE
    • Offline and Online: ContextualAnomalyDetector

NAB score

  • Get score: GetDetectorScore
  • Normalize score: NormalizeScore + GetNullAndPerfectScores

False Positve Reduction

  • Offline and Online: ReduceAnomalies

Static or interactive visualizations

  • Offline: PlotDetections

NOTE: As usual in R, the documentation pages for each function can be loaded from the command line with the commands ? or help:

?CpSdEwma
help(CpSdEwma)

Example

This is a basic example of the use of otsad package:

library(otsad)

## basic example code

# Generate data
set.seed(100)
n <- 500
x <- sample(1:100, n, replace = TRUE)
x[70:90] <- sample(110:115, 21, replace = TRUE) # distributional shift
x[25] <- 200 # abrupt transient anomaly
x[320] <- 170 # abrupt transient anomaly
df <- data.frame(timestamp = 1:n, value = x)

# Apply classic processing SD-EWMA detector
result <- CpSdEwma(data = df$value, n.train = 5, threshold = 0.01, l = 3)
res <- cbind(df, result)
PlotDetections(res, title = "SD-EWMA ANOMALY DETECTOR", return.ggplot = TRUE)

See plotly interactive graph

For more details, see otsad documentation and vignettes.

About

❗ This is a read-only mirror of the CRAN R package repository. otsad — Online Time Series Anomaly Detectors. Homepage: https://github.com/alaineiturria/otsad Report bugs for this package: https://github.com/alaineiturria/otsad/issues

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