CHAMP: Changepoint Detection Using Approximate Model Parameters

Abstract

We introduce CHAMP, an algorithm for online Bayesian changepoint detection in settings where it is difficult or undesirable to integrate over the parameters of candidate models. Rather than requiring integration of the parameters of candidate models as in several other Bayesian approaches, we require only the ability to fit model parameters to data segments. This approach greatly simplifies the use of Bayesian changepoint detection, allows it to be used with many more types of models, and improves performance when detecting parameter changes within a single model. Experimental analysis compares CHAMP to another state-of-the-art online Bayesian changepoint detection method.