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Details of Grant 

EPSRC Reference: GR/M94120/02
Title: MODELLING SHORT MULTIVARIATE TIME SERIES
Principal Investigator: Liu, Professor X
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Information Systems & Computing
Organisation: Brunel University London
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 2000 Ends: 31 March 2001 Value (£): 20,322
EPSRC Research Topic Classifications:
Artificial Intelligence Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Many sttistical Multivariate Time Series (MTS) modelling methods place constraints on the minimum number of observations in the dataset, and require distribution assumptions to be made regarding the observed time series, eg the maximum likelihood method for parameter estimation. To date, we have developed a fast and approxiamte method based on evolutionary programming techniques to locate variables that are highly correlated within high-dimensional MTS. We have also demonstrated the promises of automated model order selection and parameter estimation using genetic algorithms. Specifically the method bypassed the size restrictions of the statistical mehtods, made no distribution assumptions, and also located the order and associated parameters as a whole step. The proposed research will extend the current work on modelling MTS data into a coherent methodology for forecasting purposes. This will be achieved by developing methods for model selection based on the current variable selection work, by improving the existing mehtods for model order selection and parameter estimation, and by integrating the above into an effective forecasting methodology. This methodology will be tested on medical and process data.
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Organisation Website: http://www.brunel.ac.uk