High-dimensional, multiscale online changepoint detection

Modern technology has not only allowed the collection of data sets of unprecedented size, but has also facilitated the real-time monitoring of many types of evolving processes of interest. Wearable health devices, astronomical survey telescopes, self-driving cars and transport network load-tracking systems are just a few examples of new technologies that collect large quantities of streaming data, and that provide new challenges and opportunities for statisticians. Very often, a key feature of interest in the monitoring of a data stream is a changepoint; that is, a moment in time at which the data generating mechanism undergoes a change. Such times often represent events of interest, e.g. a change in heart function, and moreover, the accurate identification of changepoints often facilitates the decomposition of a data stream into stationary segments. Historically, it has tended to be univariate time series that have been monitored and studied, within the well-established field of statistical process control (e.g.