A Flexible Neuro-Fuzzy Autoregressive Technique for Non-linear Time Series Forecasting

  • Authors:

  • Alejandro Veloz;Héctor Allende-Cid;Héctor Allende;Claudio Moraga;Rodrigo Salas

  • Affiliations:

  • Depto. de Ingeniería Biomédica, Universidad de Valparaíso, Valparaíso, Chile and Depto. de Informática, Universidad Técnica Federico Santa María, Valparaíso ...;Depto. de Informática, Universidad Técnica Federico Santa María, Valparaíso, Chile;Depto. de Informática, Universidad Técnica Federico Santa María, Valparaíso, Chile;European Centre for Soft Computing, Mieres, Spain E-33600 and Dortmund University of Technology, Dortmund, Germany 44221;Depto. de Ingeniería Biomédica, Universidad de Valparaíso, Valparaíso, Chile

  • Venue:
  • KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
  • Year:
  • 2009

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Abstract

The aim of this paper is to simultaneously identify and estimate a non-linear autoregressive time series using a flexible neuro-fuzzy model. We provide a self organization and incremental mechanism to the adaptation process of the neuro-fuzzy model. The self organization mechanism searches for a suitable set of premises and consequents to enhance the time series estimation performance, while the incremental method selects influential lags in the model description. Experimental results indicate that our proposal reliably identifies appropriate lags for non-linear time series. Our proposal is illustrated by simulations on both synthetic and real data.