Change detection

In statistical analysis, change detection tries to identify changes in the probability distribution of a stochastic process. More generally it also includes the detection of anomalous behavior.

Online change detection
Using the sequential analysis ("online") approach, any change test must make a trade-off between these common metrics:
 * False alarm rate
 * Misdetection rate
 * Detection delay

Bayes change detection
In a Bayes change-detection problem, a prior distribution is available for the change time.

Minimax change detection
In minimax change detection, the objective is to minimize the expected detection delay for some worst-case change-time distribution, subject to a cost or constraint on false alarms.

A key technique for minimax change detection is the CUSUM procedure.

Offline change detection
Offline algorithms may employ clustering based on maximum likelihood estimation.

Applications of change detection
Change detection tests are often used in manufacturing (quality control), intrusion detection, spam filtering, and medical diagnostics.

Notes and references