Nonlinear prediction of time series

K.C Nisbet, S. McLaughlin, B. Mulgrew

Research output: Chapter in Book/Report/Conference proceedingConference contribution


There is considerable interest in the use of nonlinear techniques to perform prediction of naturally occurring time series, e.g. medical signals and signals from seismic returns. The motivation in considering these techniques lies in the fact that may of the underlying generation mechanisms are nonlinear. There has been growing interest in the use of neural network architectures for such applications. This has been coupled with the research carried out in the field of nonlinear dynamical systems, especially so-called chaotic systems. This paper reports an investigation of the prediction of a chaotic time series arising from a well-known differential equation. A series approach is applied to the estimation of such a series as well as the more regular and well-understood periodic case. First of all, the radial basis function technique is applied, using a approach based on the Wiener theory. A brief comparison is made with the same sort of approach applied to the Volterra series predictor (Nisbet et al., 1991)
Original languageEnglish
Title of host publication1991 Second International Conference on Artificial Neural Networks
Number of pages4
ISBN (Print)0852965311
Publication statusPublished - 1991
Externally publishedYes
Event 1991 Second International Conference on Artificial Neural Networks - Bournemouth, United Kingdom
Duration: 18 Nov 199120 Nov 2991


Conference 1991 Second International Conference on Artificial Neural Networks
Country/TerritoryUnited Kingdom


  • neural network architectures
  • nonlinear dynamical systems
  • chaotic systems ,
  • chaotic time series
  • differential equation
  • radial basis function
  • Wiener theory
  • volterra series predictor


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