Nonlinear modelling of air pollution time series

R. J. Foxall, I. Krcmar, G. C. Cawley, S. R. Dorling, D. P. Mandic

Research output: Contribution to conferencePaper

4 Citations (Scopus)

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
Original languageEnglish
Pages3505-3508
Number of pages4
DOIs
Publication statusPublished - May 2001
EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2001) - Salt Lake City, UT , United States
Duration: 7 May 200111 May 2001

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2001)
CountryUnited States
CitySalt Lake City, UT
Period7/05/0111/05/01

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