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Foxall, R. J., Krcmar, I., Cawley, G. C. 
ORCID: https://orcid.org/0000-0002-4118-9095, Dorling, S. R. and Mandic, D. P.
(2001)
Nonlinear modelling of air pollution time series.
In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2001), 2001-05-07 - 2001-05-11.
Abstract
An analysis of predictability of a nonlinear and nonstationary ozone time series is provided. For rigour, the deterministic versus stochastic (DVS) analysis is first undertaken to detect and measure inherent nonlinearity of the data. Based upon this, neural and linear adaptive predictors are compared on this time series for various filter orders, hence indicating the embedding dimension. Simulation results confirm the analysis and show that for this class of air pollution data, neural, especially recurrent neural predictors, perform best
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Water Security Research Centre Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 23 Jul 2011 19:11 |
| Last Modified: | 22 Apr 2023 03:35 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/23226 |
| DOI: | 10.1109/ICASSP.2001.940597 |
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