# Time Series - Breakout detection

### Table of Contents

## About

Breakout occurs in time series data and have two characteristics:

- A Mean shift: A sudden jump in the time series corresponds to a mean shift. A sudden jump in CPU utilization from 40% to 60% would exemplify a mean shift.
- A Ramp up: A gradual increase in the value of the metric from one steady state to another constitutes a ramp up. A gradual increase in CPU utilization from 40% to 60% would exemplify a ramp up.

Time series often contain more than one breakout.

Breakouts detection must be robust, from a statistical standpoint, in the presence of anomalies.

## Utilization

Breakout detection can be used to detect

- change in user engagement (such as during popular live events such as the Oscars, Super Bowl and World Cup.)
- hardware issues (breakouts in time series data of system metrics)
- in user engagement post an A/B test
- …

where:

- The two red vertical lines denote the locations of the breakouts detected
- we can see that the detection is robust to anomalies (the peaks)

## Twitter R Package

The underlying algorithm of the R package– 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.

## Documentation / Reference

- The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series.