Novel statistical methods for detecting anomalies in data streams.

传感器的低成本意味着许多机械设备的性能，从飞机引擎到路由器，现在可以连续监测。这样做是为了检测底层设备的问题，以便采取行动。然而，收集的数据量已经变得如此之大，以至于人工检查不再可能。这使得监视性能数据的自动化方法不可或缺。我的博士专注于开发新的统计方法来检测这些数据流中的异常或非典型行为。更有效的方法可以发现更广泛的异常情况，从而可以更早地发现问题，从而减少其影响。异常检测方法也用于一系列其他应用，从欺诈预防到网络安全。具体的研究问题包括：(1)如何区分不同类型的异常；开发可扩展到高维和高频数据流的统计算法；（iii）了解新统计方法的理论性质。与bt合作。该项目涉及统计与应用概率（计算统计、统计方法学、时间序列）领域。

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).