Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution

Christoph Bergmeir, Isaac Triguero, Francisco Velasco, José Manuel Benítez

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This paper presents a procedure for parameter estimation of the neuro-coefficient smooth transition autoregressive model, substituting the combination of grid search and local search of the original proposal of Medeiros and Veiga (2005, IEEE Trans. NN, 16(1):97-113) with a differential evolution scheme. The purpose of this novel fitting procedure is to obtain more accurate models under preservation of the most important model characteristics. These are, firstly, that the models are built using an iterative approach based on statistical tests, and therewith have a mathematically sound construction procedure. And secondly, that the models are interpretable in terms of fuzzy rules. The proposed procedure has been tested empirically by applying it to different real-world time series. The results indicate that, in terms of accuracy, significantly improved models can be achieved, so that accuracy of the resulting models is comparable to other standard time series forecasting methods.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 7th International Conference, HAIS 2012, Proceedings
PublisherSpringer-Verlag London Ltd.
Number of pages10
Volume7208 LNAI
EditionPART 1
ISBN (Print)9783642289415
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Hybrid Artificial Intelligence Systems 2012 - Salamanca, Spain
Duration: 28 Mar 201230 Mar 2012
Conference number: 7th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7208 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Conference on Hybrid Artificial Intelligence Systems 2012
Abbreviated titleHAIS 2012


  • differential evolution
  • statistical models
  • threshold autoregressive models
  • time series

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