anomalousACM v0.1.0

Unusual Time Series Detection

Methods for detecting anomalous time series.

Readme

Anomalous time-series R Package

It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on server performance are collected every hour for each of thousands of servers. A common use-case is to identify servers that are behaving unusually. Methods in this package compute a vector of features on each time series, measuring characteristics of the series. For example, the features may include lag correlation, strength of seasonality, spectral entropy, etc. Then a robust principal component decomposition is used on the features, and various bivariate outlier detection methods are applied to the first two principal components. This enables the most unusual series, based on their feature vectors, to be identified. The bivariate outlier detection methods used are based on highest density regions and alpha-hulls. For demo purposes, this package contains both synthetic and real data from Yahoo.

A cut-down version of this package under a GPL licence is available from http://github.com/robjhyndman/anomalous.

Installation

You can install the package using

# install.packages("devtools")
devtools::install_github("robjhyndman/anomalous-acm")

Simple Example

  z <- ts(matrix(rnorm(3000),ncol=100),freq=4)
  y <- tsmeasures(z)
  biplot.features(y)
  anomaly(y)

License

This package is free and open source software, licensed under ACM.

Functions in anomalousACM

Name Description
dat5 Synthetic time series
anomaly Anomalous time-series detection
dat2 Aggregated and anonymized datasets from Yahoo representing server metrics of Yahoo services
dat0 Aggregated and anonymized datasets from Yahoo representing server metrics of Yahoo services
dat4 Synthetic time series
biplot biplot of (robust) PCA components of the feature matrix
dat1 Aggregated and anonymized datasets from Yahoo representing server metrics of Yahoo services
tsmeasures Computes a feature matrix of a set of time-series
dat3 Aggregated and anonymized datasets from Yahoo representing server metrics of Yahoo services
anomalous-package Unusual Time Series Detection
No Results!

Details

Type Package
LazyLoad yes
LazyData yes
ByteCompile TRUE
BugReports https://github.com/robjhyndman/anomalous-acm/issues
License ACM

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