A Change Dynamic Model for the Online Detection of Gradual Change
Abstract
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 process behavior. In cases where these transitions occur gradually, this assumption can result in a reduced ability to properly identify and respond to process change. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in a Bayesian framework, 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, where we find our change-dynamic model can enable faster and more accurate identification of gradual change than traditional change-point models allow.
Keywords
Cite
@article{arxiv.2205.01054,
title = {A Change Dynamic Model for the Online Detection of Gradual Change},
author = {Chris Browne},
journal= {arXiv preprint arXiv:2205.01054},
year = {2022}
}