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Change detection

In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes. In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes. Specific applications, like step detection and edge detection, may be concerned with changes in the mean, variance, correlation, or spectral density of the process. More generally change detection also includes the detection of anomalous behavior: anomaly detection. Using the sequential analysis ('online') approach, any change test must make a trade-off between these common metrics: In a Bayes change-detection problem, a prior distribution is available for the change time. Online change detection is also done using streaming algorithms. 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. Basseville (1993, Section 2.6) discusses offline change-in-mean detection with hypothesis testing based on the works of Page and Picard and maximum-likelihood estimation of the change time, related to two-phase regression.Other approaches employ clustering based on maximum likelihood estimation.

[ "Computer vision", "Remote sensing", "Artificial intelligence", "Pattern recognition", "Image differencing", "Change detection and notification", "radiometric normalization", "Change blindness", "change detection algorithms" ]
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