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On Nonlinear Processing of Air Pollution Data

  • Rob Foxall
  • Igor Krcmar
  • Gavin Cawley
  • Stephen Dorling
  • Danilo P. Mandic

Abstract

Three methods — DVS plots, attractor reconstruction, and variance analysis of delay vectors — for detecting nonlinearities in time series are compared on an air pollution dataset. For rigour each method is also used on a surrogate dataset, based on a high-order linear fit to the original data. Finally, a comparison of a standard linear analysis to a neural network model analysis of the air pollution dataset is provided.

Keywords

Time Series Prediction Normalize Little Mean Square Local Linear Model Linear Activation Function Nonlinear Activation Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Rob Foxall
    • 1
  • Igor Krcmar
    • 2
  • Gavin Cawley
    • 1
  • Stephen Dorling
    • 3
  • Danilo P. Mandic
    • 1
  1. 1.School of Information SytemsUniversity of East AngliaNorwichUK
  2. 2.Faculty of Electrical EngineeringUniversity of BanjalukaBanjalukaBosnia and Herzegovina
  3. 3.School of Environmental SciencesUniversity of East AngliaNorwichUK

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