Change detection

In statistical analysis, change detection tries to identify changes in the probability distribution of a stochastic process^[1]. 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.

See also

• Structural break -- Change in model structure
• Change detection (GIS)
• Detection theory
• Hypothesis testing
• Recall rate
• Receiver operating characteristic

Notes and references

1. ^ A specific application may be concerned with changes in the mean, variance, correlation, or spectral density

• Michèle Basseville and Igor V. Nikiforov (April 1993). Detection of Abrupt Changes: Theory and Application. Prentice-Hall, Englewood Cliffs, N.J.. ISBN 0-13-126780-9. http://www.irisa.fr/sisthem/kniga/. 

• H. Vincent Poor and Olympia Hadjiliadis (2009). Quickest Detection. Cambridge University Press. ISBN 978-0-521-62104-5. 

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