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.
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.
Install the R package using the following commands on the R console:
install.packages("devtools")
devtools::install_github("twitter/BreakoutDetection")
library(BreakoutDetection)
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.
help(breakout)
To get started, the user is recommended to use the example dataset which comes with the packages. Execute the following commands:
data(Scribe)
res = breakout(Scribe, min.size=24, method='multi', beta=.001, degree=1, plot=TRUE)
res$plot
The above yields the following plot:
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. Unlike the existing approaches mentioned earlier, EDM is robust in the presence of anomalies. The change in mean in the time series can be better viewed with the following annotated plot:
The horizontal lines in the annotated plot above correspond to the approximate (i.e., filtering out the effect of anomalies) mean for each window.
We thank James Tsiamis and Scott Wong for their support and Nicholas James as the primary researcher behind this research.