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README.md

README.md

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")

Example

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

library(otsad)

## basic example code
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)


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)

For more details, see otsad documentation and vignettes.

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