A Change Dynamic Model for the Online Detection of Gradual Change

In the field of change-detection changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in distribution. In contrast, many real world processes undergo more gradual change in their behavior. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in which change-points are used within a hierarchical model to indicate moments of gradual change onset or termination. We apply this model to synthetic data and EEG readings drawn during epileptic seizure, finding that our model can afford faster and more accurate identification of gradual change than traditional change-point models allow.