Network Informed Methods for High Dimensional Time Series

Many real world data sets consist of data that changes over time, called a time series. For example, the daily average temperature in a particular region or the return on investment of some financial asset. Often,we are interested in multiple time series and the co-movement between them. If we can understand the co-movement, this can be used for improved predictions. For example, in environmental science, the concentration of various pollutants over time along with meteorological indicators can be used to predictair quality, which has a strong association with increased risk of cardiovascular and respiratory diseases. In this project we propose a new model and estimation technique for multiple time series prediction. The estimation procedure uses ideas from network science. We aim to understand the mathematical properties of the proposed model and investigate whether its predictions outperform other models on a variety ofapplication data sets.

This project falls within the EPSRC MathematicalSciences research area

The primary CDT impact will be training 75 PhD graduates as the next generation of leaders in statistics and statistical machine learning. These graduates will lead in industry, government, health care, and academic research. They will bridge the gap between academia and industry, resulting in significant knowledge transfer to both established and start-up companies. Because this cohort will also learn to mentor other researchers, the CDT will ultimately address a UK-wide skills gap. The students will also be crucial in keeping the UK at the forefront of methodological research in statistics and machine learning.
After graduating, students will act as multipliers, educating others in advanced methodology throughout their career. There are a range of further impacts:
- The CDT has a large number of high calibre external partners in government, health care, industry and science. These partnerships will catalyse immediate knowledge transfer, bringing cutting edge methodology to a large number of areas. Knowledge transfer will also be achieved through internships/placements of our students with users of statistics and machine learning.
- Our Women in Mathematics and Statistics summer programme is aimed at students who could go on to apply for a PhD. This programme will inspire the next generation of statisticians and also provide excellent leadership training for the CDT students.
- The students will develop new methodology and theory in the domains of statistics and statistical machine learning. It will be relevant research, addressing the key questions behind real world problems. The research will be published in the best possible statistics journals and machine learning conferences and will be made available online. To maximize reproducibility and replicability, source code and replication files will be made available as open source software or, when relevant to an industrial collaboration, held as a patent or software copyright.