GTΔ: Detecting temporal changes in group stochastic processes

Given a portfolio of stocks or a series of frames in a video how do we detect significant changes in a group of values for real-time applications? In this article, we formalize the problem of sequentially detecting temporal changes in a group of stochastic processes. As a solution to this particular problem, we propose the group temporal change (GTΔ) algorithm, a simple yet effective technique for the sequential detection of significant changes in a variety of statistical properties of a group over time. Due to the flexible framework of the GTΔ algorithm, a domain expert is able to select one or more statistical properties that they are interested in monitoring. The usefulness of our proposed algorithm is also demonstrated against state-of-the-art techniques on synthetically generated data as well as on two real-world applications; a portfolio of healthcare stocks over a 20 year period and a video monitoring the activity of our Sun.