UOS
Novel Time Series Machine Learning Methodology for High-Dimensional Data
✓ Fully Funded
⏰ Closing Soon
🎓 Data Analysis
🎓 Econometrics
🎓 Statistics
funded PhD
anomaly detection
forecasting
high-dimensional data
missing data imputation
probabilistic forecasting
time series machine learning
transformers
Funded PhD at the University of Strathclyde focused on new machine learning methods for imputation, forecasting, and anomaly detection in high-dimensional time series data.
Project Description
This PhD project focuses on developing novel time series machine learning methodology for high-dimensional data, with emphasis on missing value imputation and accurate forecasting.
The research is especially focused on difficult cases involving high-dimensional and discrete-valued data, where existing methods remain limited.
The project has two main research areas:
Imputation of missing data in high-dimensional time series
modelling network relationships between components
combining temporal dependency with state-space methods
applying dimension reduction through factor models
developing self-exciting spatio-temporal models
handling both continuous and discrete-valued data
Machine learning architectures for robust forecasting
developing deep learning models based on temporal convolutional networks and transformers
extending forecasting methods to high-dimensional and discrete-valued settings
combining factor models with probabilistic and statistical hybrid methods
improving uncertainty quantification using Bayesian inference and quantile regression
The project will also validate the methods using real-world case studies in:
finance
healthcare
environmental monitoring
Expected outputs include journal publications, open-source AI models, and deployment-ready prototypes.
Entry Requirements
Strong background in mathematics, statistics, econometrics, machine learning, data science, or related field
Interest in time series modelling, forecasting, and missing data methods
Strong quantitative and analytical skills
Preferred:
Interest in deep learning
Interest in probabilistic modelling
Experience with high-dimensional dataset
Interest in time series modelling, forecasting, and missing data methods
Strong quantitative and analytical skills
Preferred:
Interest in deep learning
Interest in probabilistic modelling
Experience with high-dimensional dataset
How to Apply
Contact the supervisors:
Dr Jiazhu Pan
Prof Ke Chen
Apply through the University of Strathclyde PhD application route
Include the project title and supervisor details in the application
Dr Jiazhu Pan
Prof Ke Chen
Apply through the University of Strathclyde PhD application route
Include the project title and supervisor details in the application
Eligibility
UK/Home
EU
International
Supervisor Profile
DJ
Dr Jiazhu Pan, Prof Ke Chen
University of Strathclyde, Department of Mathematics & Statistics
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