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EPSRC Reference:
GR/R35018/01
Title:
Explaining Multivariate Time Series to Detect Early Problem Signs
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 February 2002
Ends:
31 January 2004
Value (£):
100,974
EPSRC Research Topic Classifications:
Artificial Intelligence
Intelligent & Expert Systems
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel Date
Panel Name
Outcome
25 Apr 2001
Software Technologies 25/26 April
Deferred
Summary on Grant Application Form
There has been a limited amount of work on the learning of explanation models directly from multivariate time series (MTS) data. This type of research is especially important for those applications where there is a wealth of MTS data but there is no well-established domain theory or rich body of relevant domain experience, and where detecting potential problems at an early stage is crucial. Over the last few years we have researched the issues related to the learning of such models and made important progress which calls for further investigation. This proposal aims to extend our current work into a coherent computational framework that is able to produce reliable and timely explanations from MTS data. This will be achieved by developing a number of advanced methods for learning efficient and reliable MTS explanation models, by integrating these methods into an effective computational framework, and by testing this framework on a variety of synthetic and real-world MTS data, including eye screening data from Moorfields Eye Hospital, oil refinery data from BP-Amoco, and virus gene expression data from the Windeyer Institute of Medical Sciences.
Key Findings
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Potential use in non-academic contexts
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Description
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Summary
Date Materialised
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Project URL:
Further Information:
Organisation Website:
http://www.brunel.ac.uk