Online Changepoint Detection on a Budget
This addresses the need for efficient real-time change detection in applications like monitoring, but it is incremental as it builds on existing online methods with added tuning.
The paper tackles the problem of detecting abrupt distribution changes in data streams with strict computational constraints, proposing an online algorithm that matches offline performance while using constant storage and per-observation complexity.
Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithms which operate in an online setting in the sense that both its storage requirements and worst-case computational complexity per observation are independent of the number of previous observations. We propose an online changepoint detection algorithm for both univariate and multivariate data which compares favorably with offline changepoint detection algorithms while also operating in a strictly more constrained computational model. In addition, we present a simple online hyperparameter auto tuning technique for these algorithms.