  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.
