Identification of Lags in Nonlinear Autoregressive Time Series Using a Flexible Fuzzy Model

A. Veloz, R. Salas, H. Allende-Cid, H. Allende, C. Moraga

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


This work proposes a method to find the set of the most influential lags and the rule structure of a Takagi–Sugeno–Kang (TSK) fuzzy model for time series applications. The proposed method resembles the techniques that prioritize lags, evaluating the proximity of nearby samples in the input space using the closeness of the corresponding target values. Clusters of samples are generated, and the consistency of the mapping between the predicted variable and the set of candidate past values is evaluated. A TSK model is established, and possible redundancies in the rule base are avoided. The proposed method is evaluated using simulated and real data. Several simulation experiments were conducted for five synthetic nonlinear autoregressive processes, two nonlinear vector autoregressive processes and eight benchmark time series. The results show a competitive performance in the mean square error and a promising ability to find a proper set of lags for a given autoregressive process.

Original languageEnglish
Pages (from-to)641-666
Number of pages26
JournalNeural Processing Letters
Issue number3
StatePublished - 1 Jun 2016


  • Lags identification
  • Nonlinear autoregressive time series
  • Takagi–Sugeno–Kang fuzzy model
  • Time series analysis
  • Vector autoregressive time series


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