Change detection

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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.

Online change detection[edit]

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.

Minimax change detection[edit]

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[edit]

Offline algorithms may employ clustering based on maximum likelihood estimation.

Applications of change detection[edit]

Change detection tests are often used in manufacturing (quality control), intrusion detection, spam filtering, website tracking, and medical diagnostics. Recent approaches use change detection procedures in spatial settings, e.g., for the detection of tumors.[1]

Linguistic change detection[edit]

Linguistic change detection refers to the ability to detect word-level changes across multiple presentations of the same sentence. Researchers have found that the amount of semantic overlap (i.e., relatedness) between the changed word and the new word influences the ease with which such a detection is made (Sturt, Sanford, Stewart, & Dawydiak, 2004). Additional research has found that focussing one's attention to the word that will be changed during the initial reading of the original sentence can improve detection. This was shown using italicized text to focus attention, whereby the word that will be changing is italicized in the original sentence (Sanford, Sanford, Molle, & Emmott, 2006), as well as using clefting constructions such as "It was the tree that needed water." (Kennette, Wurm, & Van Havermaet, 2010). These change-detection phenomena appear to be robust, even occurring cross-linguistically when bilinguals read the original sentence in their native language and the changed sentence in their second language (Kennette, Wurm & Van Havermaet, 2010). Recently, researchers have detected word-level changes in semantics across time by computationally analyzing temporal corpora (for example:the word "gay" has acquired a new meaning over time) using change point detection.[2]

See also[edit]

Notes and references[edit]

  1. ^ Otto P., Schmid W. (2016), Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models, Biometrical Journal 58(5)
  2. ^ Kulkarni Vivek; Rfou Rami; Perozzi Bryan; Skiena Steven (2015). "Statistically Significant Detection of Linguistic Change". WWW '15 Proceedings of the 24th International Conference on World Wide Web.