Data Segmentation and High Dimensional Time Series Analysis

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In my research, I will study time series data segmentation, particularly multiple change point analysis for multivariate time series in the hypothesis testing framework. I will first focus on developing a moving sum procedure for low-dimensional vector autoregression processes and deriving its statistical guarantees. This will then be extended to high-dimensional data, adopting a structured VAR model for computational feasibility. I will explore the applications of proposed methodologies to economic and financial data. There is potential to study their links to modern inference problems, such as sequential testing, and to network analysis. Later into the project, I may investigate other time series structures, such as volatility models, or other types of non-stationarity, such as cointegration. As a general theme, my aim is to develop a methodology for practitioners. Therefore, along with investigating into statistical guarantees of the proposed methodologies, I will also endeavour to develop fast and efficient algorithms and make their implementations available as software packages in the R language.