Robust and Scalable Bayesian Online Changepoint Detection
This addresses the need for efficient and reliable changepoint detection in real-time data streams, though it appears incremental by improving on existing methods.
The paper tackles the problem of online changepoint detection by proposing a Bayesian approach that is robust and scalable, resulting in an algorithm that is over 10 times faster than its closest competitor.
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspective, and also addresses the scalability issues of previous attempts. Specifically, the proposed generalised Bayesian formalism leads to conjugate posteriors whose parameters are available in closed form by leveraging diffusion score matching. The resulting algorithm is exact, can be updated through simple algebra, and is more than 10 times faster than its closest competitor.