About the project
The analysis of financial risk relies on chaotic systems, which are difficult to predict due to their sensitivity and complexity. In this project, you will build on existing machine learning research to create effective prediction approaches, and work alongside experts in chaos modelling, machine learning, and finance to achieve this.
Chaos is abundant in both natural and man-made systems, from discrete event-driven dynamics like those found in financial markets, to nonlinear dynamic systems modelling weather patterns and population dynamics. Chaotic behaviours are inherently difficult to predict due to their sensitivity. Despite the ubiquity of chaos in problem domains of interest, predicting, or even bounding, the dynamics of a chaotic system remains a significant research challenge.
In this interdisciplinary project, you will develop new methods for predicting chaotic systems, building off various machine learning approaches like echo state networks. You will apply these methods to a variety of problems, from smaller well-studied systems, to more complex problems in financial risk analysis.
To support you in your project, you will be a part of the SONNETS Programme Grant, working alongside experts in high-performance computing, chaos theory, machine learning, and financial modelling. You will also have access to the University of Southampton's high-performance compute resources, including the IRIDIS compute cluster, and cloud compute resources.
