Change and Anomaly Detection in Data Streams.

It's a changing world.

Pick a system. Any will do. You could choose something simple, like the population dynamics in Lancaster's duck pond, or something fantastically complex, such as the movements of the stock markets around the world or the flow of information between every human being on the planet. Ultimately, every action in every system is governed by change and reaction to it. Every problem is a changepoint problem.

And yet, despite the increased interest in studying the detection of change in recent years, understanding in many aspects of the problem still lag behind where we would like them to be. The first major issue arises from the necessary consideration of multiple variables simultaneously. In most real-life situations, it will be necessary to examine the evolution of more than one quantity (in our duck pond example, the population of ducks and of the students who feed them would both be pertinent considerations). Yet, unpacking what a change means in this context, and how to detect it, is still very much in its infancy.

The second important strand of the project will involve speeding up existing changepoint detection methods. In order for such a detection method to be of any real world use, changes need to be detected as soon as possible, especially in situations where the nature of the change is subtle. Failure to do so can lead to the retention of policies which can be actively detrimental to the system in the medium to long term.

At the same time, however, distinguishing between true changes and mere anomalies in the data is important. Correctly identifying an anomalous occasion, and setting it apart from a true, persistent change, is vital for any decision maker. Doing this effectively and quickly, in the context with multiple data sets (known as a data stream) being observed simultaneously is the central goal of this research.

In partnership with BT.