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

被引：14

作者：

Veloz, A. ^{[1
,2
]}

Salas, R. ^{[2
]}

Allende-Cid, H. ^{[3
]}

Allende, H. ^{[1
]}

Moraga, C. ^{[4
,5
]}

机构：

[1] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile

[2] Univ Valparaiso, Escuela Ingn Biomed, Valparaiso, Chile

[3] Pontificia Univ Catolica Valparaiso, Escuela Ingn Informat, Valparaiso, Chile

[4] European Ctr Soft Comp, Mieres 33600, Spain

[5] Tech Univ Dortmund, D-44221 Dortmund, Germany

来源：

NEURAL PROCESSING LETTERS | 2016年 / 43卷 / 03期

关键词：

Lags identification; Takagi-Sugeno-Kang fuzzy model; Nonlinear autoregressive time series; Vector autoregressive time series; Time series analysis; FUNCTION APPROXIMATION; EMBEDDING DIMENSION; VECTOR QUANTIZATION; INPUT SELECTION; NEURAL-NETWORKS; ORDER; ALGORITHM; INFERENCE;

D O I：

10.1007/s11063-015-9438-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：641 / 666

页数：26

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