Breakout Detection via Robust E-Statistics
C++ R
Latest commit 7ae3fc4 Apr 15, 2015 @akejariwal akejariwal Merge pull request #15 from harsha2010/master
add static cast to disambiguate different pow methods. gives error in compilation on Mac OSX Mavericks otherwise

BreakoutDetection R package

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BreakoutDetection is an open-source R package that makes breakout detection simple and fast. The BreakoutDetection package can be used in wide variety of contexts. For example, detecting breakout in user engagement post an A/B test, detecting behavioral change, or for problems in econometrics, financial engineering, political and social sciences.

How the package works

The underlying algorithm – referred to as E-Divisive with Medians (EDM) – employs energy statistics to detect divergence in mean. Note that EDM can also be used detect change in distribution in a given time series. EDM uses robust statistical metrics, viz., median, and estimates the statistical significance of a breakout through a permutation test.

In addition, EDM is non-parametric. This is important since the distribution of production data seldom (if at all) follows the commonly assumed normal distribution or any other widely accepted model. Our experience has been that time series often contain more than one breakout. To this end, the package can also be used to detect multiple breakouts in a given time series.

How to get started

Install the R package using the following commands on the R console:


The function breakout is called to detect one or more statistically significant breakouts in the input time series. The documentation of the function breakout, which can be seen by using the following command, details the input arguments and the output of the function breakout.


A simple example

To get started, the user is recommended to use the example dataset which comes with the packages. Execute the following commands:

res = breakout(Scribe, min.size=24, method='multi', beta=.001, degree=1, plot=TRUE)

From the plot, we observe that the input time series experiences two breakouts and also has quite a few anomalies. The two red vertical lines denote the locations of the breakouts detected by the EDM algorithm.