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Changes in the statistical properties of a stochastic process are typicallyassumed to occur via change-points, which demark instantaneous moments ofcomplete and total change in process behavior. In cases where these transitionsoccur gradually, this assumption can result in a reduced ability to properlyidentify and respond to process change. With this observation in mind, weintroduce a novel change-dynamic model for the online detection of gradualchange in a Bayesian framework, in which change-points are used within ahierarchical model to indicate moments of gradual change onset or termination.We apply this model to synthetic data and EEG readings drawn during epilepticseizure, where we find our change-dynamic model can enable faster and moreaccurate identification of gradual change than traditional change-point modelsallow.

A Change Dynamic Model for the Online Detection of Gradual Change