Online Time Series Anomaly Detectors

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



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

# Get the latest development version from GitHub

Most useful functions


NAB score

False Positve Reduction

Static or interactive visualizations

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



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


## basic example code

# Generate data
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.