Scalable and Interpretable Time Series Machine Learning for Industrial Systems

About the project

About the Project Modern manufacturing, engineering and automotive systems generate vast volumes of time series data at high speed. Many leading time series classification (TSC) and regression (TSR) methods [1,2] perform well on the relatively small datasets in available benchmarks [3] but struggle as data grows in the number of cases, channels and/or series length. This limits their applicability when models must run under strict memory, energy and latency constraints at the edge. Improving these algorithms will open the door to a range of applications with potential to increase industrial efficiency. This project tackles that gap by developing TSC and TSR techniques that scale to big data and run efficiently on-chip/at the edge, making them practical for real deployments. The emphasis is on maintaining high predictive accuracy while reducing training and inference costs, enabling use in resource-constrained, time critical settings. Results will be validated on large benchmarks [4] and realistic data streams. An initial aim for the project may include improving the scalability of ensemble approaches for TSC/TSR e.g., HIVE-COTE [5]. To illustrate impact, successful applicants will explore representative use cases such as predictive maintenance and prognostics in vehicles and industrial systems, optimising manufacturing processes such as moulding, and monitoring smart materials/structures. There will be opportunity for local and international collaboration throughout the project. Where possible, the student will leverage the University of Bradford’s engineering experience and collection of industrial datasets to ground the work in real data. Successful applicants will engage with an international network of TSML researchers based around the open-source aeon toolkit to support reproducibility and uptake. Depending on applicant interest, there may be scope for additional/alternative applications such as digital health and human activity recognition using wearable devices. How to apply Formal applications can be submitted via the University of Bradford web site . Applicants should register an account, select 'Postgraduate Research' as the course type and use the keywords 'computer science'. Please include the project title on the Research Proposal section; applicants are not required to supply a research proposal for this project. Informal enquiries are also welcome. About the University of Bradford Bradford is a research-active University supporting the highest-quality research. We excel in applying our research to benefit our stakeholders by working with employers and organisations world-wide across the private, public, voluntary and community sectors and actively encourage and support our postgraduate researchers to engage in research and business development activities. Positive Action Statement At the University of Bradford our vision is a world of inclusion and equality of opportunity, where people want to, and can, make a difference. We place equality and diversity, inclusion, and a commitment to social mobility at the centre of our mission and ethos. In working to make a difference we are committed to addressing systemic inequality and disadvantages experienced by Black, Asian and Minority Ethnic staff and students. Under sections 158-159 of the Equality Act 2010, positive action can be taken where protected group members are under-represented. At Bradford, our data show that people from Black, Asian, and Minority Ethnic groups who are UK nationals are significantly under-represented at the postgraduate researcher level. These are lawful measures designed to address systemic and structural issues which result in the under-representation of Black, Asian, and Minority Ethnic students in PGR studies.

Research areas