Title :
Nonlinear modelling of air pollution time series
Author :
Foxall, Rob ; Krcmar, Igor ; Cawley, Gavin ; Dorling, Steve ; Mandic, Danilo P.
Author_Institution :
Sch. of Inf. Syst., East Anglia Univ., Norwich, UK
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
Keywords :
adaptive estimation; adaptive filters; air pollution; filtering theory; nonlinear filters; ozone; prediction theory; recurrent neural nets; time series; air pollution; deterministic versus stochastic analysis; embedding dimension; filter orders; linear adaptive predictors; nonlinear modelling; nonstationary ozone time series; predictability; recurrent neural predictors; Adaptive filters; Air pollution; Analytical models; Atmospheric measurements; Nonlinear filters; Pollution measurement; Predictive models; Stochastic processes; Time series analysis; Voltage control;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940597
