A flexible neuro-fuzzy autoregressive technique for non-linear time series forecasting

Alejandro Veloz, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende, Claudio Moraga, Rodrigo Salas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information and Engineering Systems - 13th International Conference, KES 2009, Proceedings
Pages22-29
Number of pages8
EditionPART 1
DOIs
StatePublished - 2009
Externally publishedYes
Event13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009 - Santiago, Chile
Duration: 28 Sep 200930 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5711 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2009
CountryChile
CitySantiago
Period28/09/0930/09/09

Keywords

  • Flexible and Incremental learning
  • Neuro-fuzzy models
  • Non-linear Autoregressive Time Series

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