Novel statistical methods for detecting anomalies in data streams.

The low cost of sensors means that the performance of many mechanical devices, from plane engines to routers, is now monitored continuously. This is done in order to detect problems with the underlying device in order to allow for action to be taken. However, the amount of data gathered has become so large that manual inspection is no longer possible. This makes automated methods to monitor performance data indispensable. My PhD focusses on developing novel statistical methods to detect anomalies, or untypical behaviour, in such data streams. More effective methods would allow to detect a wider range of anomalies, which in turn would allow to detect problems earlier, thus reducing their impact. Anomaly detection methods are also used for a range of other applications ranging from fraud prevention to cyber security. Specific research questions include (i) how to differentiate between different types of anomaly; (ii) developing statistical algorithms that can scale to high-dimensional and high-frequency data streams; (iii) understanding the theoretical properties of the new statistical methods. In partnership with BT. This project lies with the area of Statistics and Applied Probability (Computational Statistics, Statistical Methodology, Time Series).