SIFAR: Self-identification of lags of an autoregressive TSK-based model

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

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

Abstract

In this work, a Takagi-Sugeno-Kang (TSK) model is used for time series analysis and some important questions about the identification of this kind of models are addressed: the identification of the model structure and the set of the most influential regressors or lags. The main idea behind of the proposed method resembles to those techniques that prioritize lags evaluating the proximity of nearby samples in the input space in relation to the closeness of the corresponding target values. Clusters of samples are generated and the consistence of the mapping between the predicted variable and the set of candidate past values is evaluated. Afterwards, a TSK model is established and the redundancies in the rule base are avoided. Simulation experiments were conducted for 2 synthetic nonlinear autoregressive processes and for 4 benchmark time series. Results show a promising performance in terms of forecasting error and in terms of ability to find a proper set of lags of a given autoregressive process.

Original languageEnglish
Title of host publicationProceedings - IEEE 42nd International Symposium on Multiple-Valued Logic, ISMVL 2012
Pages226-231
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event42nd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2012 - Victoria, BC, Canada
Duration: 14 May 201216 May 2012

Publication series

NameProceedings of The International Symposium on Multiple-Valued Logic
ISSN (Print)0195-623X

Conference

Conference42nd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2012
Country/TerritoryCanada
CityVictoria, BC
Period14/05/1216/05/12

Keywords

  • Lags identification
  • nonlinear autoregressive time series models
  • Takagi-Sugeno-Kang fuzzy model

Fingerprint

Dive into the research topics of 'SIFAR: Self-identification of lags of an autoregressive TSK-based model'. Together they form a unique fingerprint.

Cite this