Reading Documents for Bayesian Online Change Point Detection
Taehoon Kim, Jaesik Choi · 2015
Modeling non-stationary time-series data for making predictions is a challenging but important task.One of the key issues is to identify long-term changes accurately in time-varying data.Bayesian Online Change Point Detection (BO-CPD) algorithms efficiently detect long-term changes without assuming the Markov property which is vulnerable to local signal noise.We propose a Document based BO-CPD (DBO-CPD) model which automatically detects long-term temporal changes of continuous variables based on a novel dynamic Bayesian analysis which combines a non-parametric regression, the Gaussian Process (GP), with generative models of texts such as news articles and posts on social networks.Since texts often include important clues of signal changes, DBO-CPD enables the accurate prediction of long-term changes accurately.We show that our algorithm outperforms existing BO-CPDs in two real-world datasets: stock prices and movie revenues.