# A Change Dynamic Model for the Online Detection of Gradual Change

arXiv: https://arxiv.org/abs/2205.01054 · HTML: https://mlsanity.com/paper/2205.01054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 2 of 2)

- [Automatic Construction and Natural-Language Description of Nonparametric Regression Models](https://mlsanity.com/paper/1402.4304/) (2014)
- [Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces](https://mlsanity.com/paper/1511.04408/) (2015)

## Abstract (arXiv)

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.