| EPSRC Reference: |
GR/K77884/01 |
| Title: |
IDENTIFICATION, ESTIMATION AND FORECASTING METHODS FOR A CLASS OF NONLINEAR STOCHASTIC TIME SERIES |
| Principal Investigator: |
Young, Professor PC |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Environmental Science |
| Organisation: |
Lancaster University |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 July 1996 |
Ends: |
30 June 1999 |
Value (£): |
104,153
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| Environment |
Energy |
| Transport Systems and Vehicles |
Water |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
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This research project combines the multi-disciplinary by complementary expertise of the PI & co-investigators in order to develop a new approach to the identification, estimation and forecasting of a particular, but widely applicable, class of models for nonlinear and nonstationary stochastic time series. Particular attention will be devoted to ensuring that this approach yields models that can be interpreted in practically useful and physically meaningful terms (Data-Based Mechanistic, DMB modelling). The study, which exploits novel methods of recursive estimation, will involve the development and empirical investigation of the combined non-parametric and parametric methods that form the basis of the proposed modelling procedure. This will involve practical examples carried out in associated with outside organisations, including the modelling and forescasting of nonlinear rainfall-flow processes, inter-urban road traffic, flows, and electricity demand data; and the development of nonlinear models for the forecasting and adaptive, optimal control of plant growth in horticultural glasshouses. Associated theoretical studies will be concerned with establishing the consistency and, if possible, efficiency and bias of the estimation procedures. In addition, the resulting methodology will be compared with alternative identification and estimation procedures, including neural network methods and an orthogonal approach to NARMAX modelling.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.lancs.ac.uk |